diff --git a/lectures/makemore/.ipynb_checkpoints/makemore_part1_bigrams-checkpoint.ipynb b/lectures/makemore/.ipynb_checkpoints/makemore_part1_bigrams-checkpoint.ipynb
new file mode 100644
index 00000000..fbe58a86
--- /dev/null
+++ b/lectures/makemore/.ipynb_checkpoints/makemore_part1_bigrams-checkpoint.ipynb
@@ -0,0 +1,1913 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "words = open('names.txt', 'r').read().splitlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['emma',\n",
+       " 'olivia',\n",
+       " 'ava',\n",
+       " 'isabella',\n",
+       " 'sophia',\n",
+       " 'charlotte',\n",
+       " 'mia',\n",
+       " 'amelia',\n",
+       " 'harper',\n",
+       " 'evelyn']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "words[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32033"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(len(w) for w in words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(len(w) for w in words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = {}\n",
+    "for w in words:\n",
+    "  chs = ['<S>'] + list(w) + ['<E>']\n",
+    "  for ch1, ch2 in zip(chs, chs[1:]):\n",
+    "    bigram = (ch1, ch2)\n",
+    "    b[bigram] = b.get(bigram, 0) + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(('n', '<E>'), 6763),\n",
+       " (('a', '<E>'), 6640),\n",
+       " (('a', 'n'), 5438),\n",
+       " (('<S>', 'a'), 4410),\n",
+       " (('e', '<E>'), 3983),\n",
+       " (('a', 'r'), 3264),\n",
+       " (('e', 'l'), 3248),\n",
+       " (('r', 'i'), 3033),\n",
+       " (('n', 'a'), 2977),\n",
+       " (('<S>', 'k'), 2963),\n",
+       " (('l', 'e'), 2921),\n",
+       " (('e', 'n'), 2675),\n",
+       " (('l', 'a'), 2623),\n",
+       " (('m', 'a'), 2590),\n",
+       " (('<S>', 'm'), 2538),\n",
+       " (('a', 'l'), 2528),\n",
+       " (('i', '<E>'), 2489),\n",
+       " (('l', 'i'), 2480),\n",
+       " (('i', 'a'), 2445),\n",
+       " (('<S>', 'j'), 2422),\n",
+       " (('o', 'n'), 2411),\n",
+       " (('h', '<E>'), 2409),\n",
+       " (('r', 'a'), 2356),\n",
+       " (('a', 'h'), 2332),\n",
+       " (('h', 'a'), 2244),\n",
+       " (('y', 'a'), 2143),\n",
+       " (('i', 'n'), 2126),\n",
+       " (('<S>', 's'), 2055),\n",
+       " (('a', 'y'), 2050),\n",
+       " (('y', '<E>'), 2007),\n",
+       " (('e', 'r'), 1958),\n",
+       " (('n', 'n'), 1906),\n",
+       " (('y', 'n'), 1826),\n",
+       " (('k', 'a'), 1731),\n",
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+       " (('y', 'h'), 22),\n",
+       " (('r', 'w'), 21),\n",
+       " (('s', 'b'), 21),\n",
+       " (('m', 'n'), 20),\n",
+       " (('f', 'l'), 20),\n",
+       " (('w', 's'), 20),\n",
+       " (('k', 'k'), 20),\n",
+       " (('h', 'z'), 20),\n",
+       " (('g', 'd'), 19),\n",
+       " (('l', 'h'), 19),\n",
+       " (('n', 'm'), 19),\n",
+       " (('x', 'z'), 19),\n",
+       " (('u', 'f'), 19),\n",
+       " (('f', 't'), 18),\n",
+       " (('l', 'r'), 18),\n",
+       " (('p', 't'), 17),\n",
+       " (('t', 'c'), 17),\n",
+       " (('k', 't'), 17),\n",
+       " (('d', 'v'), 17),\n",
+       " (('u', 'p'), 16),\n",
+       " (('p', 'l'), 16),\n",
+       " (('l', 'w'), 16),\n",
+       " (('p', 's'), 16),\n",
+       " (('o', 'j'), 16),\n",
+       " (('r', 'q'), 16),\n",
+       " (('y', 'p'), 15),\n",
+       " (('l', 'p'), 15),\n",
+       " (('t', 'v'), 15),\n",
+       " (('r', 'p'), 14),\n",
+       " (('l', 'n'), 14),\n",
+       " (('e', 'q'), 14),\n",
+       " (('f', 'y'), 14),\n",
+       " (('s', 'v'), 14),\n",
+       " (('u', 'j'), 14),\n",
+       " (('v', 'l'), 14),\n",
+       " (('q', 'a'), 13),\n",
+       " (('u', 'y'), 13),\n",
+       " (('q', 'i'), 13),\n",
+       " (('w', 'l'), 13),\n",
+       " (('p', 'y'), 12),\n",
+       " (('y', 'f'), 12),\n",
+       " (('c', 'q'), 11),\n",
+       " (('j', 'r'), 11),\n",
+       " (('n', 'w'), 11),\n",
+       " (('n', 'f'), 11),\n",
+       " (('t', 'w'), 11),\n",
+       " (('m', 'z'), 11),\n",
+       " (('u', 'o'), 10),\n",
+       " (('f', 'u'), 10),\n",
+       " (('l', 'z'), 10),\n",
+       " (('h', 'w'), 10),\n",
+       " (('u', 'q'), 10),\n",
+       " (('j', 'y'), 10),\n",
+       " (('s', 'z'), 10),\n",
+       " (('s', 'd'), 9),\n",
+       " (('j', 'l'), 9),\n",
+       " (('d', 'j'), 9),\n",
+       " (('k', 'm'), 9),\n",
+       " (('r', 'f'), 9),\n",
+       " (('h', 'j'), 9),\n",
+       " (('v', 'n'), 8),\n",
+       " (('n', 'b'), 8),\n",
+       " (('i', 'w'), 8),\n",
+       " (('h', 'b'), 8),\n",
+       " (('b', 's'), 8),\n",
+       " (('w', 't'), 8),\n",
+       " (('w', 'd'), 8),\n",
+       " (('v', 'v'), 7),\n",
+       " (('v', 'u'), 7),\n",
+       " (('j', 's'), 7),\n",
+       " (('m', 'j'), 7),\n",
+       " (('f', 's'), 6),\n",
+       " (('l', 'g'), 6),\n",
+       " (('l', 'j'), 6),\n",
+       " (('j', 'w'), 6),\n",
+       " (('n', 'x'), 6),\n",
+       " (('y', 'q'), 6),\n",
+       " (('w', 'k'), 6),\n",
+       " (('g', 'm'), 6),\n",
+       " (('x', 'u'), 5),\n",
+       " (('m', 'h'), 5),\n",
+       " (('m', 'l'), 5),\n",
+       " (('j', 'm'), 5),\n",
+       " (('c', 's'), 5),\n",
+       " (('j', 'v'), 5),\n",
+       " (('n', 'p'), 5),\n",
+       " (('d', 'f'), 5),\n",
+       " (('x', 'd'), 5),\n",
+       " (('z', 'b'), 4),\n",
+       " (('f', 'n'), 4),\n",
+       " (('x', 'c'), 4),\n",
+       " (('m', 't'), 4),\n",
+       " (('t', 'm'), 4),\n",
+       " (('z', 'n'), 4),\n",
+       " (('z', 't'), 4),\n",
+       " (('p', 'u'), 4),\n",
+       " (('c', 'z'), 4),\n",
+       " (('b', 'n'), 4),\n",
+       " (('z', 's'), 4),\n",
+       " (('f', 'w'), 4),\n",
+       " (('d', 't'), 4),\n",
+       " (('j', 'd'), 4),\n",
+       " (('j', 'c'), 4),\n",
+       " (('y', 'w'), 4),\n",
+       " (('v', 'k'), 3),\n",
+       " (('x', 'w'), 3),\n",
+       " (('t', 'j'), 3),\n",
+       " (('c', 'j'), 3),\n",
+       " (('q', 'w'), 3),\n",
+       " (('g', 'b'), 3),\n",
+       " (('o', 'q'), 3),\n",
+       " (('r', 'x'), 3),\n",
+       " (('d', 'c'), 3),\n",
+       " (('g', 'j'), 3),\n",
+       " (('x', 'f'), 3),\n",
+       " (('z', 'w'), 3),\n",
+       " (('d', 'k'), 3),\n",
+       " (('u', 'u'), 3),\n",
+       " (('m', 'v'), 3),\n",
+       " (('c', 'x'), 3),\n",
+       " (('l', 'q'), 3),\n",
+       " (('p', 'b'), 2),\n",
+       " (('t', 'g'), 2),\n",
+       " (('q', 's'), 2),\n",
+       " (('t', 'x'), 2),\n",
+       " (('f', 'k'), 2),\n",
+       " (('b', 't'), 2),\n",
+       " (('j', 'n'), 2),\n",
+       " (('k', 'c'), 2),\n",
+       " (('z', 'k'), 2),\n",
+       " (('s', 'j'), 2),\n",
+       " (('s', 'f'), 2),\n",
+       " (('z', 'j'), 2),\n",
+       " (('n', 'q'), 2),\n",
+       " (('f', 'z'), 2),\n",
+       " (('h', 'g'), 2),\n",
+       " (('w', 'w'), 2),\n",
+       " (('k', 'j'), 2),\n",
+       " (('j', 'k'), 2),\n",
+       " (('w', 'm'), 2),\n",
+       " (('z', 'c'), 2),\n",
+       " (('z', 'v'), 2),\n",
+       " (('w', 'f'), 2),\n",
+       " (('q', 'm'), 2),\n",
+       " (('k', 'z'), 2),\n",
+       " (('j', 'j'), 2),\n",
+       " (('z', 'p'), 2),\n",
+       " (('j', 't'), 2),\n",
+       " (('k', 'b'), 2),\n",
+       " (('m', 'w'), 2),\n",
+       " (('h', 'f'), 2),\n",
+       " (('c', 'g'), 2),\n",
+       " (('t', 'f'), 2),\n",
+       " (('h', 'c'), 2),\n",
+       " (('q', 'o'), 2),\n",
+       " (('k', 'd'), 2),\n",
+       " (('k', 'v'), 2),\n",
+       " (('s', 'g'), 2),\n",
+       " (('z', 'd'), 2),\n",
+       " (('q', 'r'), 1),\n",
+       " (('d', 'z'), 1),\n",
+       " (('p', 'j'), 1),\n",
+       " (('q', 'l'), 1),\n",
+       " (('p', 'f'), 1),\n",
+       " (('q', 'e'), 1),\n",
+       " (('b', 'c'), 1),\n",
+       " (('c', 'd'), 1),\n",
+       " (('m', 'f'), 1),\n",
+       " (('p', 'n'), 1),\n",
+       " (('w', 'b'), 1),\n",
+       " (('p', 'c'), 1),\n",
+       " (('h', 'p'), 1),\n",
+       " (('f', 'h'), 1),\n",
+       " (('b', 'j'), 1),\n",
+       " (('f', 'g'), 1),\n",
+       " (('z', 'g'), 1),\n",
+       " (('c', 'p'), 1),\n",
+       " (('p', 'k'), 1),\n",
+       " (('p', 'm'), 1),\n",
+       " (('x', 'n'), 1),\n",
+       " (('s', 'q'), 1),\n",
+       " (('k', 'f'), 1),\n",
+       " (('m', 'k'), 1),\n",
+       " (('x', 'h'), 1),\n",
+       " (('g', 'f'), 1),\n",
+       " (('v', 'b'), 1),\n",
+       " (('j', 'p'), 1),\n",
+       " (('g', 'z'), 1),\n",
+       " (('v', 'd'), 1),\n",
+       " (('d', 'b'), 1),\n",
+       " (('v', 'h'), 1),\n",
+       " (('h', 'h'), 1),\n",
+       " (('g', 'v'), 1),\n",
+       " (('d', 'q'), 1),\n",
+       " (('x', 'b'), 1),\n",
+       " (('w', 'z'), 1),\n",
+       " (('h', 'q'), 1),\n",
+       " (('j', 'b'), 1),\n",
+       " (('x', 'm'), 1),\n",
+       " (('w', 'g'), 1),\n",
+       " (('t', 'b'), 1),\n",
+       " (('z', 'x'), 1)]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(b.items(), key = lambda kv: -kv[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 365,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = torch.zeros((27, 27), dtype=torch.int32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 366,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chars = sorted(list(set(''.join(words))))\n",
+    "stoi = {s:i+1 for i,s in enumerate(chars)}\n",
+    "stoi['.'] = 0\n",
+    "itos = {i:s for s,i in stoi.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "for w in words:\n",
+    "  chs = ['.'] + list(w) + ['.']\n",
+    "  for ch1, ch2 in zip(chs, chs[1:]):\n",
+    "    ix1 = stoi[ch1]\n",
+    "    ix2 = stoi[ch2]\n",
+    "    N[ix1, ix2] += 1\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x1152 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.figure(figsize=(16,16))\n",
+    "plt.imshow(N, cmap='Blues')\n",
+    "for i in range(27):\n",
+    "    for j in range(27):\n",
+    "        chstr = itos[i] + itos[j]\n",
+    "        plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n",
+    "        plt.text(j, i, N[i, j].item(), ha=\"center\", va=\"top\", color='gray')\n",
+    "plt.axis('off');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "N[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([0.0000, 0.1377, 0.0408, 0.0481, 0.0528, 0.0478, 0.0130, 0.0209, 0.0273,\n",
+       "        0.0184, 0.0756, 0.0925, 0.0491, 0.0792, 0.0358, 0.0123, 0.0161, 0.0029,\n",
+       "        0.0512, 0.0642, 0.0408, 0.0024, 0.0117, 0.0096, 0.0042, 0.0167, 0.0290])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = N[0].float()\n",
+    "p = p / p.sum()\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'m'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g = torch.Generator().manual_seed(2147483647)\n",
+    "ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
+    "itos[ix]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([0.6064, 0.3033, 0.0903])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g = torch.Generator().manual_seed(2147483647)\n",
+    "p = torch.rand(3, generator=g)\n",
+    "p = p / p.sum()\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([1, 1, 2, 0, 0, 2, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 2, 0, 0,\n",
+       "        1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
+       "        0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
+       "        0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 1, 0,\n",
+       "        0, 1, 1, 1])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.multinomial(p, num_samples=100, replacement=True, generator=g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([3])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([27, 27])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "P.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([27, 1])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "P.sum(1, keepdim=True).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 27, 27\n",
+    "# 27,  1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([27])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "P.sum(1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 27, 27\n",
+    "#  1, 27"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P = (N+1).float()\n",
+    "P /= P.sum(1, keepdims=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mor.\n",
+      "axx.\n",
+      "minaymoryles.\n",
+      "kondlaisah.\n",
+      "anchshizarie.\n"
+     ]
+    }
+   ],
+   "source": [
+    "g = torch.Generator().manual_seed(2147483647)\n",
+    "\n",
+    "for i in range(5):\n",
+    "  \n",
+    "  out = []\n",
+    "  ix = 0\n",
+    "  while True:\n",
+    "    p = P[ix]\n",
+    "    ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
+    "    out.append(itos[ix])\n",
+    "    if ix == 0:\n",
+    "      break\n",
+    "  print(''.join(out))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GOAL: maximize likelihood of the data w.r.t. model parameters (statistical modeling)\n",
+    "# equivalent to maximizing the log likelihood (because log is monotonic)\n",
+    "# equivalent to minimizing the negative log likelihood\n",
+    "# equivalent to minimizing the average negative log likelihood\n",
+    "\n",
+    "# log(a*b*c) = log(a) + log(b) + log(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 435,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "log_likelihood=tensor(-564996.8125, grad_fn=<AddBackward0>)\n",
+      "nll=tensor(564996.8125, grad_fn=<NegBackward0>)\n",
+      "2.476470470428467\n"
+     ]
+    }
+   ],
+   "source": [
+    "log_likelihood = 0.0\n",
+    "n = 0\n",
+    "\n",
+    "for w in words:\n",
+    "#for w in [\"andrejq\"]:\n",
+    "  chs = ['.'] + list(w) + ['.']\n",
+    "  for ch1, ch2 in zip(chs, chs[1:]):\n",
+    "    ix1 = stoi[ch1]\n",
+    "    ix2 = stoi[ch2]\n",
+    "    prob = P[ix1, ix2]\n",
+    "    logprob = torch.log(prob)\n",
+    "    log_likelihood += logprob\n",
+    "    n += 1\n",
+    "    #print(f'{ch1}{ch2}: {prob:.4f} {logprob:.4f}')\n",
+    "\n",
+    "print(f'{log_likelihood=}')\n",
+    "nll = -log_likelihood\n",
+    "print(f'{nll=}')\n",
+    "print(f'{nll/n}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 449,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ". e\n",
+      "e m\n",
+      "m m\n",
+      "m a\n",
+      "a .\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create the training set of bigrams (x,y)\n",
+    "xs, ys = [], []\n",
+    "\n",
+    "for w in words[:1]:\n",
+    "  chs = ['.'] + list(w) + ['.']\n",
+    "  for ch1, ch2 in zip(chs, chs[1:]):\n",
+    "    ix1 = stoi[ch1]\n",
+    "    ix2 = stoi[ch2]\n",
+    "    print(ch1, ch2)\n",
+    "    xs.append(ix1)\n",
+    "    ys.append(ix2)\n",
+    "    \n",
+    "xs = torch.tensor(xs)\n",
+    "ys = torch.tensor(ys)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 450,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 0,  5, 13, 13,  1])"
+      ]
+     },
+     "execution_count": 450,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 451,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 5, 13, 13,  1,  0])"
+      ]
+     },
+     "execution_count": 451,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 487,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "         0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "         0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
+       "         0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
+       "         0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "         0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 487,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch.nn.functional as F\n",
+    "xenc = F.one_hot(xs, num_classes=27).float()\n",
+    "xenc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 488,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([5, 27])"
+      ]
+     },
+     "execution_count": 488,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xenc.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 489,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fa0dab44fa0>"
+      ]
+     },
+     "execution_count": 489,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(xenc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 490,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "execution_count": 490,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xenc.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 493,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-0.2003, -2.3711, -0.9466,  0.5369, -0.0949, -1.7872, -0.9038,  0.8194,\n",
+       "          0.6926,  0.0114, -1.5301,  0.6077, -1.2056,  1.8605, -1.3012, -0.0301,\n",
+       "         -2.1611, -0.0538, -0.0133, -0.3629,  0.5254, -0.0080,  1.1602,  1.9851,\n",
+       "          0.4976,  0.7351, -0.6373],\n",
+       "        [-0.4422,  0.5024,  1.3514, -0.4085, -0.7854, -1.2568, -0.4558,  0.1466,\n",
+       "         -0.4460,  1.2748, -0.6367,  0.6403, -0.5617, -0.3060,  1.6771, -1.4814,\n",
+       "         -2.7395,  0.3876,  0.3970,  1.5577, -0.1995, -0.1397, -1.3045,  0.4294,\n",
+       "          1.2557,  0.8007,  0.5450],\n",
+       "        [-0.2680, -0.2640,  0.4591,  0.0338,  0.7478,  1.2757, -0.9842,  0.1799,\n",
+       "          0.0824, -0.5646, -0.3657, -0.8358, -1.7654,  0.5008, -1.7455, -0.8160,\n",
+       "         -2.2721,  0.9713, -1.0734,  0.3115, -0.2506,  0.0757,  0.9332,  1.6536,\n",
+       "          1.2306,  0.1231, -0.2530],\n",
+       "        [-0.2680, -0.2640,  0.4591,  0.0338,  0.7478,  1.2757, -0.9842,  0.1799,\n",
+       "          0.0824, -0.5646, -0.3657, -0.8358, -1.7654,  0.5008, -1.7455, -0.8160,\n",
+       "         -2.2721,  0.9713, -1.0734,  0.3115, -0.2506,  0.0757,  0.9332,  1.6536,\n",
+       "          1.2306,  0.1231, -0.2530],\n",
+       "        [ 0.1949, -1.1315,  0.9479, -0.6382, -0.4422, -0.6489,  0.6576, -1.9004,\n",
+       "          2.0254,  1.2617, -1.7238,  1.2971, -0.6925, -0.3873,  0.7874, -0.8088,\n",
+       "          0.5746, -0.5263, -0.5928,  0.1419,  1.0683, -0.1760, -0.3507, -0.5358,\n",
+       "          0.1470,  1.5682, -1.0393]])"
+      ]
+     },
+     "execution_count": 493,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "W = torch.randn((27, 1))\n",
+    "xenc @ W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 506,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.0205, 0.0023, 0.0097, 0.0428, 0.0228, 0.0042, 0.0101, 0.0568, 0.0500,\n",
+       "         0.0253, 0.0054, 0.0460, 0.0075, 0.1609, 0.0068, 0.0243, 0.0029, 0.0237,\n",
+       "         0.0247, 0.0174, 0.0423, 0.0248, 0.0799, 0.1822, 0.0412, 0.0522, 0.0132],\n",
+       "        [0.0154, 0.0397, 0.0928, 0.0160, 0.0110, 0.0068, 0.0152, 0.0278, 0.0154,\n",
+       "         0.0860, 0.0127, 0.0456, 0.0137, 0.0177, 0.1286, 0.0055, 0.0016, 0.0354,\n",
+       "         0.0357, 0.1141, 0.0197, 0.0209, 0.0065, 0.0369, 0.0844, 0.0535, 0.0414],\n",
+       "        [0.0212, 0.0213, 0.0439, 0.0287, 0.0586, 0.0994, 0.0104, 0.0332, 0.0301,\n",
+       "         0.0158, 0.0192, 0.0120, 0.0047, 0.0458, 0.0048, 0.0123, 0.0029, 0.0733,\n",
+       "         0.0095, 0.0379, 0.0216, 0.0299, 0.0705, 0.1450, 0.0950, 0.0314, 0.0215],\n",
+       "        [0.0212, 0.0213, 0.0439, 0.0287, 0.0586, 0.0994, 0.0104, 0.0332, 0.0301,\n",
+       "         0.0158, 0.0192, 0.0120, 0.0047, 0.0458, 0.0048, 0.0123, 0.0029, 0.0733,\n",
+       "         0.0095, 0.0379, 0.0216, 0.0299, 0.0705, 0.1450, 0.0950, 0.0314, 0.0215],\n",
+       "        [0.0289, 0.0077, 0.0613, 0.0126, 0.0153, 0.0124, 0.0459, 0.0036, 0.1801,\n",
+       "         0.0839, 0.0042, 0.0869, 0.0119, 0.0161, 0.0522, 0.0106, 0.0422, 0.0140,\n",
+       "         0.0131, 0.0274, 0.0692, 0.0199, 0.0167, 0.0139, 0.0275, 0.1140, 0.0084]])"
+      ]
+     },
+     "execution_count": 506,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logits = xenc @ W # log-counts\n",
+    "counts = logits.exp() # equivalent N\n",
+    "probs = counts / counts.sum(1, keepdims=True)\n",
+    "probs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 509,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([0.0205, 0.0023, 0.0097, 0.0428, 0.0228, 0.0042, 0.0101, 0.0568, 0.0500,\n",
+       "        0.0253, 0.0054, 0.0460, 0.0075, 0.1609, 0.0068, 0.0243, 0.0029, 0.0237,\n",
+       "        0.0247, 0.0174, 0.0423, 0.0248, 0.0799, 0.1822, 0.0412, 0.0522, 0.0132])"
+      ]
+     },
+     "execution_count": 509,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 510,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([27])"
+      ]
+     },
+     "execution_count": 510,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs[0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 507,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(1.)"
+      ]
+     },
+     "execution_count": 507,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs[0].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# (5, 27) @ (27, 27) -> (5, 27)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SUMMARY ------------------------------>>>>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 528,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 0,  5, 13, 13,  1])"
+      ]
+     },
+     "execution_count": 528,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 529,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 5, 13, 13,  1,  0])"
+      ]
+     },
+     "execution_count": 529,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 557,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# randomly initialize 27 neurons' weights. each neuron receives 27 inputs\n",
+    "g = torch.Generator().manual_seed(2147483647)\n",
+    "W = torch.randn((27, 27), generator=g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 558,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xenc = F.one_hot(xs, num_classes=27).float() # input to the network: one-hot encoding\n",
+    "logits = xenc @ W # predict log-counts\n",
+    "counts = logits.exp() # counts, equivalent to N\n",
+    "probs = counts / counts.sum(1, keepdims=True) # probabilities for next character\n",
+    "# btw: the last 2 lines here are together called a 'softmax'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 559,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([5, 27])"
+      ]
+     },
+     "execution_count": 559,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 560,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------\n",
+      "bigram example 1: .e (indexes 0,5)\n",
+      "input to the neural net: 0\n",
+      "output probabilities from the neural net: tensor([0.0607, 0.0100, 0.0123, 0.0042, 0.0168, 0.0123, 0.0027, 0.0232, 0.0137,\n",
+      "        0.0313, 0.0079, 0.0278, 0.0091, 0.0082, 0.0500, 0.2378, 0.0603, 0.0025,\n",
+      "        0.0249, 0.0055, 0.0339, 0.0109, 0.0029, 0.0198, 0.0118, 0.1537, 0.1459])\n",
+      "label (actual next character): 5\n",
+      "probability assigned by the net to the the correct character: 0.012286253273487091\n",
+      "log likelihood: -4.3992743492126465\n",
+      "negative log likelihood: 4.3992743492126465\n",
+      "--------\n",
+      "bigram example 2: em (indexes 5,13)\n",
+      "input to the neural net: 5\n",
+      "output probabilities from the neural net: tensor([0.0290, 0.0796, 0.0248, 0.0521, 0.1989, 0.0289, 0.0094, 0.0335, 0.0097,\n",
+      "        0.0301, 0.0702, 0.0228, 0.0115, 0.0181, 0.0108, 0.0315, 0.0291, 0.0045,\n",
+      "        0.0916, 0.0215, 0.0486, 0.0300, 0.0501, 0.0027, 0.0118, 0.0022, 0.0472])\n",
+      "label (actual next character): 13\n",
+      "probability assigned by the net to the the correct character: 0.018050702288746834\n",
+      "log likelihood: -4.014570713043213\n",
+      "negative log likelihood: 4.014570713043213\n",
+      "--------\n",
+      "bigram example 3: mm (indexes 13,13)\n",
+      "input to the neural net: 13\n",
+      "output probabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n",
+      "        0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n",
+      "        0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126])\n",
+      "label (actual next character): 13\n",
+      "probability assigned by the net to the the correct character: 0.026691533625125885\n",
+      "log likelihood: -3.623408794403076\n",
+      "negative log likelihood: 3.623408794403076\n",
+      "--------\n",
+      "bigram example 4: ma (indexes 13,1)\n",
+      "input to the neural net: 13\n",
+      "output probabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n",
+      "        0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n",
+      "        0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126])\n",
+      "label (actual next character): 1\n",
+      "probability assigned by the net to the the correct character: 0.07367684692144394\n",
+      "log likelihood: -2.6080667972564697\n",
+      "negative log likelihood: 2.6080667972564697\n",
+      "--------\n",
+      "bigram example 5: a. (indexes 1,0)\n",
+      "input to the neural net: 1\n",
+      "output probabilities from the neural net: tensor([0.0150, 0.0086, 0.0396, 0.0100, 0.0606, 0.0308, 0.1084, 0.0131, 0.0125,\n",
+      "        0.0048, 0.1024, 0.0086, 0.0988, 0.0112, 0.0232, 0.0207, 0.0408, 0.0078,\n",
+      "        0.0899, 0.0531, 0.0463, 0.0309, 0.0051, 0.0329, 0.0654, 0.0503, 0.0091])\n",
+      "label (actual next character): 0\n",
+      "probability assigned by the net to the the correct character: 0.0149775305762887\n",
+      "log likelihood: -4.201204299926758\n",
+      "negative log likelihood: 4.201204299926758\n",
+      "=========\n",
+      "average negative log likelihood, i.e. loss = 3.7693049907684326\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "nlls = torch.zeros(5)\n",
+    "for i in range(5):\n",
+    "  # i-th bigram:\n",
+    "  x = xs[i].item() # input character index\n",
+    "  y = ys[i].item() # label character index\n",
+    "  print('--------')\n",
+    "  print(f'bigram example {i+1}: {itos[x]}{itos[y]} (indexes {x},{y})')\n",
+    "  print('input to the neural net:', x)\n",
+    "  print('output probabilities from the neural net:', probs[i])\n",
+    "  print('label (actual next character):', y)\n",
+    "  p = probs[i, y]\n",
+    "  print('probability assigned by the net to the the correct character:', p.item())\n",
+    "  logp = torch.log(p)\n",
+    "  print('log likelihood:', logp.item())\n",
+    "  nll = -logp\n",
+    "  print('negative log likelihood:', nll.item())\n",
+    "  nlls[i] = nll\n",
+    "\n",
+    "print('=========')\n",
+    "print('average negative log likelihood, i.e. loss =', nlls.mean().item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 561,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# --------- !!! OPTIMIZATION !!! yay --------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 565,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 0,  5, 13, 13,  1])"
+      ]
+     },
+     "execution_count": 565,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 566,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 5, 13, 13,  1,  0])"
+      ]
+     },
+     "execution_count": 566,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 580,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# randomly initialize 27 neurons' weights. each neuron receives 27 inputs\n",
+    "g = torch.Generator().manual_seed(2147483647)\n",
+    "W = torch.randn((27, 27), generator=g, requires_grad=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 602,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# forward pass\n",
+    "xenc = F.one_hot(xs, num_classes=27).float() # input to the network: one-hot encoding\n",
+    "logits = xenc @ W # predict log-counts\n",
+    "counts = logits.exp() # counts, equivalent to N\n",
+    "probs = counts / counts.sum(1, keepdims=True) # probabilities for next character\n",
+    "loss = -probs[torch.arange(5), ys].log().mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 603,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.6891887187957764\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(loss.item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 604,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# backward pass\n",
+    "W.grad = None # set to zero the gradient\n",
+    "loss.backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 605,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W.data += -0.1 * W.grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 606,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# --------- !!! OPTIMIZATION !!! yay, but this time actually --------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 682,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of examples:  228146\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create the dataset\n",
+    "xs, ys = [], []\n",
+    "for w in words:\n",
+    "  chs = ['.'] + list(w) + ['.']\n",
+    "  for ch1, ch2 in zip(chs, chs[1:]):\n",
+    "    ix1 = stoi[ch1]\n",
+    "    ix2 = stoi[ch2]\n",
+    "    xs.append(ix1)\n",
+    "    ys.append(ix2)\n",
+    "xs = torch.tensor(xs)\n",
+    "ys = torch.tensor(ys)\n",
+    "num = xs.nelement()\n",
+    "print('number of examples: ', num)\n",
+    "\n",
+    "# initialize the 'network'\n",
+    "g = torch.Generator().manual_seed(2147483647)\n",
+    "W = torch.randn((27, 27), generator=g, requires_grad=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 716,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.481828451156616\n"
+     ]
+    }
+   ],
+   "source": [
+    "# gradient descent\n",
+    "for k in range(1):\n",
+    "  \n",
+    "  # forward pass\n",
+    "  xenc = F.one_hot(xs, num_classes=27).float() # input to the network: one-hot encoding\n",
+    "  logits = xenc @ W # predict log-counts\n",
+    "  counts = logits.exp() # counts, equivalent to N\n",
+    "  probs = counts / counts.sum(1, keepdims=True) # probabilities for next character\n",
+    "  loss = -probs[torch.arange(num), ys].log().mean() + 0.01*(W**2).mean()\n",
+    "  print(loss.item())\n",
+    "  \n",
+    "  # backward pass\n",
+    "  W.grad = None # set to zero the gradient\n",
+    "  loss.backward()\n",
+    "  \n",
+    "  # update\n",
+    "  W.data += -50 * W.grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 725,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mor.\n",
+      "axx.\n",
+      "minaymoryles.\n",
+      "kondlaisah.\n",
+      "anchthizarie.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# finally, sample from the 'neural net' model\n",
+    "g = torch.Generator().manual_seed(2147483647)\n",
+    "\n",
+    "for i in range(5):\n",
+    "  \n",
+    "  out = []\n",
+    "  ix = 0\n",
+    "  while True:\n",
+    "    \n",
+    "    # ----------\n",
+    "    # BEFORE:\n",
+    "    #p = P[ix]\n",
+    "    # ----------\n",
+    "    # NOW:\n",
+    "    xenc = F.one_hot(torch.tensor([ix]), num_classes=27).float()\n",
+    "    logits = xenc @ W # predict log-counts\n",
+    "    counts = logits.exp() # counts, equivalent to N\n",
+    "    p = counts / counts.sum(1, keepdims=True) # probabilities for next character\n",
+    "    # ----------\n",
+    "    \n",
+    "    ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
+    "    out.append(itos[ix])\n",
+    "    if ix == 0:\n",
+    "      break\n",
+    "  print(''.join(out))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lectures/makemore/.ipynb_checkpoints/makemore_part2_mlp-checkpoint.ipynb b/lectures/makemore/.ipynb_checkpoints/makemore_part2_mlp-checkpoint.ipynb
new file mode 100644
index 00000000..75d6dc47
--- /dev/null
+++ b/lectures/makemore/.ipynb_checkpoints/makemore_part2_mlp-checkpoint.ipynb
@@ -0,0 +1,607 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "import matplotlib.pyplot as plt # for making figures\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read in all the words\n",
+    "words = open('names.txt', 'r').read().splitlines()\n",
+    "words[:8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build the vocabulary of characters and mappings to/from integers\n",
+    "chars = sorted(list(set(''.join(words))))\n",
+    "stoi = {s:i+1 for i,s in enumerate(chars)}\n",
+    "stoi['.'] = 0\n",
+    "itos = {i:s for s,i in stoi.items()}\n",
+    "print(itos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build the dataset\n",
+    "\n",
+    "block_size = 3 # context length: how many characters do we take to predict the next one?\n",
+    "X, Y = [], []\n",
+    "for w in words:\n",
+    "  \n",
+    "  #print(w)\n",
+    "  context = [0] * block_size\n",
+    "  for ch in w + '.':\n",
+    "    ix = stoi[ch]\n",
+    "    X.append(context)\n",
+    "    Y.append(ix)\n",
+    "    #print(''.join(itos[i] for i in context), '--->', itos[ix])\n",
+    "    context = context[1:] + [ix] # crop and append\n",
+    "  \n",
+    "X = torch.tensor(X)\n",
+    "Y = torch.tensor(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape, X.dtype, Y.shape, Y.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 769,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([182441, 3]) torch.Size([182441])\n",
+      "torch.Size([22902, 3]) torch.Size([22902])\n",
+      "torch.Size([22803, 3]) torch.Size([22803])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# build the dataset\n",
+    "block_size = 3 # context length: how many characters do we take to predict the next one?\n",
+    "\n",
+    "def build_dataset(words):  \n",
+    "  X, Y = [], []\n",
+    "  for w in words:\n",
+    "\n",
+    "    #print(w)\n",
+    "    context = [0] * block_size\n",
+    "    for ch in w + '.':\n",
+    "      ix = stoi[ch]\n",
+    "      X.append(context)\n",
+    "      Y.append(ix)\n",
+    "      #print(''.join(itos[i] for i in context), '--->', itos[ix])\n",
+    "      context = context[1:] + [ix] # crop and append\n",
+    "\n",
+    "  X = torch.tensor(X)\n",
+    "  Y = torch.tensor(Y)\n",
+    "  print(X.shape, Y.shape)\n",
+    "  return X, Y\n",
+    "\n",
+    "import random\n",
+    "random.seed(42)\n",
+    "random.shuffle(words)\n",
+    "n1 = int(0.8*len(words))\n",
+    "n2 = int(0.9*len(words))\n",
+    "\n",
+    "Xtr, Ytr = build_dataset(words[:n1])\n",
+    "Xdev, Ydev = build_dataset(words[n1:n2])\n",
+    "Xte, Yte = build_dataset(words[n2:])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C = torch.randn((27, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb = C[X]\n",
+    "emb.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W1 = torch.randn((6, 100))\n",
+    "b1 = torch.randn(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = torch.tanh(emb.view(-1, 6) @ W1 + b1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W2 = torch.randn((100, 27))\n",
+    "b2 = torch.randn(27)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logits = h @ W2 + b2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logits.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "counts = logits.exp()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prob = counts / counts.sum(1, keepdims=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prob.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = -prob[torch.arange(32), Y].log().mean()\n",
+    "loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ------------ now made respectable :) ---------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 780,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([182441, 3]), torch.Size([182441]))"
+      ]
+     },
+     "execution_count": 780,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Xtr.shape, Ytr.shape # dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 790,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = torch.Generator().manual_seed(2147483647) # for reproducibility\n",
+    "C = torch.randn((27, 10), generator=g)\n",
+    "W1 = torch.randn((30, 200), generator=g)\n",
+    "b1 = torch.randn(200, generator=g)\n",
+    "W2 = torch.randn((200, 27), generator=g)\n",
+    "b2 = torch.randn(27, generator=g)\n",
+    "parameters = [C, W1, b1, W2, b2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 791,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11897"
+      ]
+     },
+     "execution_count": 791,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(p.nelement() for p in parameters) # number of parameters in total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 792,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p in parameters:\n",
+    "  p.requires_grad = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 793,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lre = torch.linspace(-3, 0, 1000)\n",
+    "lrs = 10**lre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 794,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lri = []\n",
+    "lossi = []\n",
+    "stepi = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 795,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(200000):\n",
+    "  \n",
+    "  # minibatch construct\n",
+    "  ix = torch.randint(0, Xtr.shape[0], (32,))\n",
+    "  \n",
+    "  # forward pass\n",
+    "  emb = C[Xtr[ix]] # (32, 3, 10)\n",
+    "  h = torch.tanh(emb.view(-1, 30) @ W1 + b1) # (32, 200)\n",
+    "  logits = h @ W2 + b2 # (32, 27)\n",
+    "  loss = F.cross_entropy(logits, Ytr[ix])\n",
+    "  #print(loss.item())\n",
+    "  \n",
+    "  # backward pass\n",
+    "  for p in parameters:\n",
+    "    p.grad = None\n",
+    "  loss.backward()\n",
+    "  \n",
+    "  # update\n",
+    "  #lr = lrs[i]\n",
+    "  lr = 0.1 if i < 100000 else 0.01\n",
+    "  for p in parameters:\n",
+    "    p.data += -lr * p.grad\n",
+    "\n",
+    "  # track stats\n",
+    "  #lri.append(lre[i])\n",
+    "  stepi.append(i)\n",
+    "  lossi.append(loss.log10().item())\n",
+    "\n",
+    "#print(loss.item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 796,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7feda5f10250>]"
+      ]
+     },
+     "execution_count": 796,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(stepi, lossi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 797,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(2.1260, grad_fn=<NllLossBackward0>)"
+      ]
+     },
+     "execution_count": 797,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "emb = C[Xtr] # (32, 3, 2)\n",
+    "h = torch.tanh(emb.view(-1, 30) @ W1 + b1) # (32, 100)\n",
+    "logits = h @ W2 + b2 # (32, 27)\n",
+    "loss = F.cross_entropy(logits, Ytr)\n",
+    "loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 798,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(2.1701, grad_fn=<NllLossBackward0>)"
+      ]
+     },
+     "execution_count": 798,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "emb = C[Xdev] # (32, 3, 2)\n",
+    "h = torch.tanh(emb.view(-1, 30) @ W1 + b1) # (32, 100)\n",
+    "logits = h @ W2 + b2 # (32, 27)\n",
+    "loss = F.cross_entropy(logits, Ydev)\n",
+    "loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 710,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize dimensions 0 and 1 of the embedding matrix C for all characters\n",
+    "plt.figure(figsize=(8,8))\n",
+    "plt.scatter(C[:,0].data, C[:,1].data, s=200)\n",
+    "for i in range(C.shape[0]):\n",
+    "    plt.text(C[i,0].item(), C[i,1].item(), itos[i], ha=\"center\", va=\"center\", color='white')\n",
+    "plt.grid('minor')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# training split, dev/validation split, test split\n",
+    "# 80%, 10%, 10%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 805,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 10])"
+      ]
+     },
+     "execution_count": 805,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context = [0] * block_size\n",
+    "C[torch.tensor([context])].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 820,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "carmahela.\n",
+      "jhovi.\n",
+      "kimrin.\n",
+      "thil.\n",
+      "halanna.\n",
+      "jazhien.\n",
+      "amerynci.\n",
+      "aqui.\n",
+      "nellara.\n",
+      "chaiiv.\n",
+      "kaleigh.\n",
+      "ham.\n",
+      "joce.\n",
+      "quinton.\n",
+      "lilea.\n",
+      "jamilio.\n",
+      "jeron.\n",
+      "jaryni.\n",
+      "jace.\n",
+      "chrudeley.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# sample from the model\n",
+    "g = torch.Generator().manual_seed(2147483647 + 10)\n",
+    "\n",
+    "for _ in range(20):\n",
+    "    \n",
+    "    out = []\n",
+    "    context = [0] * block_size # initialize with all ...\n",
+    "    while True:\n",
+    "      emb = C[torch.tensor([context])] # (1,block_size,d)\n",
+    "      h = torch.tanh(emb.view(1, -1) @ W1 + b1)\n",
+    "      logits = h @ W2 + b2\n",
+    "      probs = F.softmax(logits, dim=1)\n",
+    "      ix = torch.multinomial(probs, num_samples=1, generator=g).item()\n",
+    "      context = context[1:] + [ix]\n",
+    "      out.append(ix)\n",
+    "      if ix == 0:\n",
+    "        break\n",
+    "    \n",
+    "    print(''.join(itos[i] for i in out))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lectures/makemore/makemore_part1_bigrams.ipynb b/lectures/makemore/makemore_part1_bigrams.ipynb
index fbe58a86..1c464937 100644
--- a/lectures/makemore/makemore_part1_bigrams.ipynb
+++ b/lectures/makemore/makemore_part1_bigrams.ipynb
@@ -1891,7 +1891,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1905,7 +1905,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.10"
   }
  },
  "nbformat": 4,
diff --git a/lectures/makemore/makemore_part2_mlp.ipynb b/lectures/makemore/makemore_part2_mlp.ipynb
index 75d6dc47..3381aeb8 100644
--- a/lectures/makemore/makemore_part2_mlp.ipynb
+++ b/lectures/makemore/makemore_part2_mlp.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,9 +14,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['emma', 'olivia', 'ava', 'isabella', 'sophia', 'charlotte', 'mia', 'amelia']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# read in all the words\n",
     "words = open('names.txt', 'r').read().splitlines()\n",
@@ -25,18 +36,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32033"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "len(words)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n"
+     ]
+    }
+   ],
    "source": [
     "# build the vocabulary of characters and mappings to/from integers\n",
     "chars = sorted(list(set(''.join(words))))\n",
@@ -48,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,25 +103,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([228146, 3]), torch.int64, torch.Size([228146]), torch.int64)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "X.shape, X.dtype, Y.shape, Y.dtype"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 769,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([182441, 3]) torch.Size([182441])\n",
-      "torch.Size([22902, 3]) torch.Size([22902])\n",
-      "torch.Size([22803, 3]) torch.Size([22803])\n"
+      "torch.Size([182625, 3]) torch.Size([182625])\n",
+      "torch.Size([22655, 3]) torch.Size([22655])\n",
+      "torch.Size([22866, 3]) torch.Size([22866])\n"
      ]
     }
    ],
@@ -130,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,9 +180,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([228146, 3, 2])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "emb = C[X]\n",
     "emb.shape"
@@ -149,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,25 +220,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-0.9641,  0.8113, -0.9821,  ...,  0.9350, -0.9989,  0.9995],\n",
+       "        [-0.4532,  0.9833,  0.7958,  ...,  0.6667, -0.4514,  1.0000],\n",
+       "        [-0.9867,  0.7630, -0.2535,  ...,  0.9740, -0.8227,  0.9992],\n",
+       "        ...,\n",
+       "        [-0.9849,  0.9582, -0.6645,  ..., -0.9313,  0.3438,  0.9800],\n",
+       "        [ 0.4796, -0.9173,  0.6425,  ...,  0.9944, -0.7974,  0.7543],\n",
+       "        [ 0.4958, -0.9482,  0.4102,  ..., -0.8575, -0.1109,  0.7206]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "h"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([228146, 100])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "h.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,7 +665,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -599,7 +679,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.10"
   }
  },
  "nbformat": 4,
diff --git a/lectures/makemore/names.txt b/lectures/makemore/names.txt
new file mode 100644
index 00000000..736d88e1
--- /dev/null
+++ b/lectures/makemore/names.txt
@@ -0,0 +1,32033 @@
+emma
+olivia
+ava
+isabella
+sophia
+charlotte
+mia
+amelia
+harper
+evelyn
+abigail
+emily
+elizabeth
+mila
+ella
+avery
+sofia
+camila
+aria
+scarlett
+victoria
+madison
+luna
+grace
+chloe
+penelope
+layla
+riley
+zoey
+nora
+lily
+eleanor
+hannah
+lillian
+addison
+aubrey
+ellie
+stella
+natalie
+zoe
+leah
+hazel
+violet
+aurora
+savannah
+audrey
+brooklyn
+bella
+claire
+skylar
+lucy
+paisley
+everly
+anna
+caroline
+nova
+genesis
+emilia
+kennedy
+samantha
+maya
+willow
+kinsley
+naomi
+aaliyah
+elena
+sarah
+ariana
+allison
+gabriella
+alice
+madelyn
+cora
+ruby
+eva
+serenity
+autumn
+adeline
+hailey
+gianna
+valentina
+isla
+eliana
+quinn
+nevaeh
+ivy
+sadie
+piper
+lydia
+alexa
+josephine
+emery
+julia
+delilah
+arianna
+vivian
+kaylee
+sophie
+brielle
+madeline
+peyton
+rylee
+clara
+hadley
+melanie
+mackenzie
+reagan
+adalynn
+liliana
+aubree
+jade
+katherine
+isabelle
+natalia
+raelynn
+maria
+athena
+ximena
+arya
+leilani
+taylor
+faith
+rose
+kylie
+alexandra
+mary
+margaret
+lyla
+ashley
+amaya
+eliza
+brianna
+bailey
+andrea
+khloe
+jasmine
+melody
+iris
+isabel
+norah
+annabelle
+valeria
+emerson
+adalyn
+ryleigh
+eden
+emersyn
+anastasia
+kayla
+alyssa
+juliana
+charlie
+esther
+ariel
+cecilia
+valerie
+alina
+molly
+reese
+aliyah
+lilly
+parker
+finley
+morgan
+sydney
+jordyn
+eloise
+trinity
+daisy
+kimberly
+lauren
+genevieve
+sara
+arabella
+harmony
+elise
+remi
+teagan
+alexis
+london
+sloane
+laila
+lucia
+diana
+juliette
+sienna
+elliana
+londyn
+ayla
+callie
+gracie
+josie
+amara
+jocelyn
+daniela
+everleigh
+mya
+rachel
+summer
+alana
+brooke
+alaina
+mckenzie
+catherine
+amy
+presley
+journee
+rosalie
+ember
+brynlee
+rowan
+joanna
+paige
+rebecca
+ana
+sawyer
+mariah
+nicole
+brooklynn
+payton
+marley
+fiona
+georgia
+lila
+harley
+adelyn
+alivia
+noelle
+gemma
+vanessa
+journey
+makayla
+angelina
+adaline
+catalina
+alayna
+julianna
+leila
+lola
+adriana
+june
+juliet
+jayla
+river
+tessa
+lia
+dakota
+delaney
+selena
+blakely
+ada
+camille
+zara
+malia
+hope
+samara
+vera
+mckenna
+briella
+izabella
+hayden
+raegan
+michelle
+angela
+ruth
+freya
+kamila
+vivienne
+aspen
+olive
+kendall
+elaina
+thea
+kali
+destiny
+amiyah
+evangeline
+cali
+blake
+elsie
+juniper
+alexandria
+myla
+ariella
+kate
+mariana
+lilah
+charlee
+daleyza
+nyla
+jane
+maggie
+zuri
+aniyah
+lucille
+leia
+melissa
+adelaide
+amina
+giselle
+lena
+camilla
+miriam
+millie
+brynn
+gabrielle
+sage
+annie
+logan
+lilliana
+haven
+jessica
+kaia
+magnolia
+amira
+adelynn
+makenzie
+stephanie
+nina
+phoebe
+arielle
+evie
+lyric
+alessandra
+gabriela
+paislee
+raelyn
+madilyn
+paris
+makenna
+kinley
+gracelyn
+talia
+maeve
+rylie
+kiara
+evelynn
+brinley
+jacqueline
+laura
+gracelynn
+lexi
+ariah
+fatima
+jennifer
+kehlani
+alani
+ariyah
+luciana
+allie
+heidi
+maci
+phoenix
+felicity
+joy
+kenzie
+veronica
+margot
+addilyn
+lana
+cassidy
+remington
+saylor
+ryan
+keira
+harlow
+miranda
+angel
+amanda
+daniella
+royalty
+gwendolyn
+ophelia
+heaven
+jordan
+madeleine
+esmeralda
+kira
+miracle
+elle
+amari
+danielle
+daphne
+willa
+haley
+gia
+kaitlyn
+oakley
+kailani
+winter
+alicia
+serena
+nadia
+aviana
+demi
+jada
+braelynn
+dylan
+ainsley
+alison
+camryn
+avianna
+bianca
+skyler
+scarlet
+maddison
+nylah
+sarai
+regina
+dahlia
+nayeli
+raven
+helen
+adrianna
+averie
+skye
+kelsey
+tatum
+kensley
+maliyah
+erin
+viviana
+jenna
+anaya
+carolina
+shelby
+sabrina
+mikayla
+annalise
+octavia
+lennon
+blair
+carmen
+yaretzi
+kennedi
+mabel
+zariah
+kyla
+christina
+selah
+celeste
+eve
+mckinley
+milani
+frances
+jimena
+kylee
+leighton
+katie
+aitana
+kayleigh
+sierra
+kathryn
+rosemary
+jolene
+alondra
+elisa
+helena
+charleigh
+hallie
+lainey
+avah
+jazlyn
+kamryn
+mira
+cheyenne
+francesca
+antonella
+wren
+chelsea
+amber
+emory
+lorelei
+nia
+abby
+april
+emelia
+carter
+aylin
+cataleya
+bethany
+marlee
+carly
+kaylani
+emely
+liana
+madelynn
+cadence
+matilda
+sylvia
+myra
+fernanda
+oaklyn
+elianna
+hattie
+dayana
+kendra
+maisie
+malaysia
+kara
+katelyn
+maia
+celine
+cameron
+renata
+jayleen
+charli
+emmalyn
+holly
+azalea
+leona
+alejandra
+bristol
+collins
+imani
+meadow
+alexia
+edith
+kaydence
+leslie
+lilith
+kora
+aisha
+meredith
+danna
+wynter
+emberly
+julieta
+michaela
+alayah
+jemma
+reign
+colette
+kaliyah
+elliott
+johanna
+remy
+sutton
+emmy
+virginia
+briana
+oaklynn
+adelina
+everlee
+megan
+angelica
+justice
+mariam
+khaleesi
+macie
+karsyn
+alanna
+aleah
+mae
+mallory
+esme
+skyla
+madilynn
+charley
+allyson
+hanna
+shiloh
+henley
+macy
+maryam
+ivanna
+ashlynn
+lorelai
+amora
+ashlyn
+sasha
+baylee
+beatrice
+itzel
+priscilla
+marie
+jayda
+liberty
+rory
+alessia
+alaia
+janelle
+kalani
+gloria
+sloan
+dorothy
+greta
+julie
+zahra
+savanna
+annabella
+poppy
+amalia
+zaylee
+cecelia
+coraline
+kimber
+emmie
+anne
+karina
+kassidy
+kynlee
+monroe
+anahi
+jaliyah
+jazmin
+maren
+monica
+siena
+marilyn
+reyna
+kyra
+lilian
+jamie
+melany
+alaya
+ariya
+kelly
+rosie
+adley
+dream
+jaylah
+laurel
+jazmine
+mina
+karla
+bailee
+aubrie
+katalina
+melina
+harlee
+elliot
+hayley
+elaine
+karen
+dallas
+irene
+lylah
+ivory
+chaya
+rosa
+aleena
+braelyn
+nola
+alma
+leyla
+pearl
+addyson
+roselyn
+lacey
+lennox
+reina
+aurelia
+noa
+janiyah
+jessie
+madisyn
+saige
+alia
+tiana
+astrid
+cassandra
+kyleigh
+romina
+stevie
+haylee
+zelda
+lillie
+aileen
+brylee
+eileen
+yara
+ensley
+lauryn
+giuliana
+livia
+anya
+mikaela
+palmer
+lyra
+mara
+marina
+kailey
+liv
+clementine
+kenna
+briar
+emerie
+galilea
+tiffany
+bonnie
+elyse
+cynthia
+frida
+kinslee
+tatiana
+joelle
+armani
+jolie
+nalani
+rayna
+yareli
+meghan
+rebekah
+addilynn
+faye
+zariyah
+lea
+aliza
+julissa
+lilyana
+anika
+kairi
+aniya
+noemi
+angie
+crystal
+bridget
+ari
+davina
+amelie
+amirah
+annika
+elora
+xiomara
+linda
+hana
+laney
+mercy
+hadassah
+madalyn
+louisa
+simone
+kori
+jillian
+alena
+malaya
+miley
+milan
+sariyah
+malani
+clarissa
+nala
+princess
+amani
+analia
+estella
+milana
+aya
+chana
+jayde
+tenley
+zaria
+itzayana
+penny
+ailani
+lara
+aubriella
+clare
+lina
+rhea
+bria
+thalia
+keyla
+haisley
+ryann
+addisyn
+amaia
+chanel
+ellen
+harmoni
+aliana
+tinsley
+landry
+paisleigh
+lexie
+myah
+rylan
+deborah
+emilee
+laylah
+novalee
+ellis
+emmeline
+avalynn
+hadlee
+legacy
+braylee
+elisabeth
+kaylie
+ansley
+dior
+paula
+belen
+corinne
+maleah
+martha
+teresa
+salma
+louise
+averi
+lilianna
+amiya
+milena
+royal
+aubrielle
+calliope
+frankie
+natasha
+kamilah
+meilani
+raina
+amayah
+lailah
+rayne
+zaniyah
+isabela
+nathalie
+miah
+opal
+kenia
+azariah
+hunter
+tori
+andi
+keily
+leanna
+scarlette
+jaelyn
+saoirse
+selene
+dalary
+lindsey
+marianna
+ramona
+estelle
+giovanna
+holland
+nancy
+emmalynn
+mylah
+rosalee
+sariah
+zoie
+blaire
+lyanna
+maxine
+anais
+dana
+judith
+kiera
+jaelynn
+noor
+kai
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\ No newline at end of file
diff --git a/lectures/micrograd/.ipynb_checkpoints/micrograd_lecture_first_half_roughly-checkpoint.ipynb b/lectures/micrograd/.ipynb_checkpoints/micrograd_lecture_first_half_roughly-checkpoint.ipynb
new file mode 100644
index 00000000..1a3fa629
--- /dev/null
+++ b/lectures/micrograd/.ipynb_checkpoints/micrograd_lecture_first_half_roughly-checkpoint.ipynb
@@ -0,0 +1,1278 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(x):\n",
+    "  return 3*x**2 - 4*x + 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(3.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f9b98432ee0>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xs = np.arange(-5, 5, 0.25)\n",
+    "ys = f(xs)\n",
+    "plt.plot(xs, ys)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.999378523327323e-06"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h = 0.000001\n",
+    "x = 2/3\n",
+    "(f(x + h) - f(x))/h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# les get more complex\n",
+    "a = 2.0\n",
+    "b = -3.0\n",
+    "c = 10.0\n",
+    "d = a*b + c\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "d1 4.0\n",
+      "d2 4.0001\n",
+      "slope 0.9999999999976694\n"
+     ]
+    }
+   ],
+   "source": [
+    "h = 0.0001\n",
+    "\n",
+    "# inputs\n",
+    "a = 2.0\n",
+    "b = -3.0\n",
+    "c = 10.0\n",
+    "\n",
+    "d1 = a*b + c\n",
+    "c += h\n",
+    "d2 = a*b + c\n",
+    "\n",
+    "print('d1', d1)\n",
+    "print('d2', d2)\n",
+    "print('slope', (d2 - d1)/h)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Value(data=-8.0)"
+      ]
+     },
+     "execution_count": 257,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class Value:\n",
+    "  \n",
+    "  def __init__(self, data, _children=(), _op='', label=''):\n",
+    "    self.data = data\n",
+    "    self.grad = 0.0\n",
+    "    self._backward = lambda: None\n",
+    "    self._prev = set(_children)\n",
+    "    self._op = _op\n",
+    "    self.label = label\n",
+    "\n",
+    "  def __repr__(self):\n",
+    "    return f\"Value(data={self.data})\"\n",
+    "  \n",
+    "  def __add__(self, other):\n",
+    "    out = Value(self.data + other.data, (self, other), '+')\n",
+    "    \n",
+    "    def _backward():\n",
+    "      self.grad += 1.0 * out.grad\n",
+    "      other.grad += 1.0 * out.grad\n",
+    "    out._backward = _backward\n",
+    "    \n",
+    "    return out\n",
+    "\n",
+    "  def __mul__(self, other):\n",
+    "    out = Value(self.data * other.data, (self, other), '*')\n",
+    "    \n",
+    "    def _backward():\n",
+    "      self.grad += other.data * out.grad\n",
+    "      other.grad += self.data * out.grad\n",
+    "    out._backward = _backward\n",
+    "      \n",
+    "    return out\n",
+    "  \n",
+    "  def tanh(self):\n",
+    "    x = self.data\n",
+    "    t = (math.exp(2*x) - 1)/(math.exp(2*x) + 1)\n",
+    "    out = Value(t, (self, ), 'tanh')\n",
+    "    \n",
+    "    def _backward():\n",
+    "      self.grad += (1 - t**2) * out.grad\n",
+    "    out._backward = _backward\n",
+    "    \n",
+    "    return out\n",
+    "  \n",
+    "  def backward(self):\n",
+    "    \n",
+    "    topo = []\n",
+    "    visited = set()\n",
+    "    def build_topo(v):\n",
+    "      if v not in visited:\n",
+    "        visited.add(v)\n",
+    "        for child in v._prev:\n",
+    "          build_topo(child)\n",
+    "        topo.append(v)\n",
+    "    build_topo(self)\n",
+    "    \n",
+    "    self.grad = 1.0\n",
+    "    for node in reversed(topo):\n",
+    "      node._backward()\n",
+    "\n",
+    "\n",
+    "a = Value(2.0, label='a')\n",
+    "b = Value(-3.0, label='b')\n",
+    "c = Value(10.0, label='c')\n",
+    "e = a*b; e.label = 'e'\n",
+    "d = e + c; d.label = 'd'\n",
+    "f = Value(-2.0, label='f')\n",
+    "L = d * f; L.label = 'L'\n",
+    "L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphviz import Digraph\n",
+    "\n",
+    "def trace(root):\n",
+    "  # builds a set of all nodes and edges in a graph\n",
+    "  nodes, edges = set(), set()\n",
+    "  def build(v):\n",
+    "    if v not in nodes:\n",
+    "      nodes.add(v)\n",
+    "      for child in v._prev:\n",
+    "        edges.add((child, v))\n",
+    "        build(child)\n",
+    "  build(root)\n",
+    "  return nodes, edges\n",
+    "\n",
+    "def draw_dot(root):\n",
+    "  dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n",
+    "  \n",
+    "  nodes, edges = trace(root)\n",
+    "  for n in nodes:\n",
+    "    uid = str(id(n))\n",
+    "    # for any value in the graph, create a rectangular ('record') node for it\n",
+    "    dot.node(name = uid, label = \"{ %s | data %.4f | grad %.4f }\" % (n.label, n.data, n.grad), shape='record')\n",
+    "    if n._op:\n",
+    "      # if this value is a result of some operation, create an op node for it\n",
+    "      dot.node(name = uid + n._op, label = n._op)\n",
+    "      # and connect this node to it\n",
+    "      dot.edge(uid + n._op, uid)\n",
+    "\n",
+    "  for n1, n2 in edges:\n",
+    "    # connect n1 to the op node of n2\n",
+    "    dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n",
+    "\n",
+    "  return dot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
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+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "draw_dot(L)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-7.286496\n"
+     ]
+    }
+   ],
+   "source": [
+    "a.data += 0.01 * a.grad\n",
+    "b.data += 0.01 * b.grad\n",
+    "c.data += 0.01 * c.grad\n",
+    "f.data += 0.01 * f.grad\n",
+    "\n",
+    "e = a * b\n",
+    "d = e + c\n",
+    "L = d * f\n",
+    "\n",
+    "print(L.data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-3.9999999999995595\n"
+     ]
+    }
+   ],
+   "source": [
+    "def lol():\n",
+    "  \n",
+    "  h = 0.001\n",
+    "  \n",
+    "  a = Value(2.0, label='a')\n",
+    "  b = Value(-3.0, label='b')\n",
+    "  c = Value(10.0, label='c')\n",
+    "  e = a*b; e.label = 'e'\n",
+    "  d = e + c; d.label = 'd'\n",
+    "  f = Value(-2.0, label='f')\n",
+    "  L = d * f; L.label = 'L'\n",
+    "  L1 = L.data\n",
+    "  \n",
+    "  a = Value(2.0, label='a')\n",
+    "  b = Value(-3.0, label='b')\n",
+    "  b.data += h\n",
+    "  c = Value(10.0, label='c')\n",
+    "  e = a*b; e.label = 'e'\n",
+    "  d = e + c; d.label = 'd'\n",
+    "  f = Value(-2.0, label='f')\n",
+    "  L = d * f; L.label = 'L'\n",
+    "  L2 = L.data\n",
+    "  \n",
+    "  print((L2 - L1)/h)\n",
+    "  \n",
+    "lol()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.arange(-5,5,0.2), np.tanh(np.arange(-5,5,0.2))); plt.grid();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inputs x1,x2\n",
+    "x1 = Value(2.0, label='x1')\n",
+    "x2 = Value(0.0, label='x2')\n",
+    "# weights w1,w2\n",
+    "w1 = Value(-3.0, label='w1')\n",
+    "w2 = Value(1.0, label='w2')\n",
+    "# bias of the neuron\n",
+    "b = Value(6.8813735870195432, label='b')\n",
+    "# x1*w1 + x2*w2 + b\n",
+    "x1w1 = x1*w1; x1w1.label = 'x1*w1'\n",
+    "x2w2 = x2*w2; x2w2.label = 'x2*w2'\n",
+    "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = 'x1*w1 + x2*w2'\n",
+    "n = x1w1x2w2 + b; n.label = 'n'\n",
+    "o = n.tanh(); o.label = 'o'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<text text-anchor=\"middle\" x=\"155\" y=\"-124.8\" font-family=\"Times,serif\" font-size=\"14.00\">grad 0.5000</text>\n",
+       "</g>\n",
+       "<!-- 140307056975984&#45;&gt;140307056976608* -->\n",
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+       "</g>\n",
+       "<!-- 140307056976512tanh -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>140307056976512tanh</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1319\" y=\"-68.8\" font-family=\"Times,serif\" font-size=\"14.00\">tanh</text>\n",
+       "</g>\n",
+       "<!-- 140307056976512tanh&#45;&gt;140307056976512 -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>140307056976512tanh&#45;&gt;140307056976512</title>\n",
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+       "<g id=\"node8\" class=\"node\">\n",
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+       "<polygon fill=\"none\" stroke=\"black\" points=\"0,-0.5 0,-36.5 200,-36.5 200,-0.5 0,-0.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"16.5\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\">w1</text>\n",
+       "<polyline fill=\"none\" stroke=\"black\" points=\"33,-0.5 33,-36.5 \"/>\n",
+       "<text text-anchor=\"middle\" x=\"75.5\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\">data &#45;3.0000</text>\n",
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+       "<text text-anchor=\"middle\" x=\"159\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\">grad 1.0000</text>\n",
+       "</g>\n",
+       "<!-- 140307056978576&#45;&gt;140307056979488* -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>140307056978576&#45;&gt;140307056979488*</title>\n",
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+       "</g>\n",
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+       "<title>140307056978624</title>\n",
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+       "<text text-anchor=\"middle\" x=\"779\" y=\"-41.8\" font-family=\"Times,serif\" font-size=\"14.00\">data 6.8814</text>\n",
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+       "<text text-anchor=\"middle\" x=\"860\" y=\"-41.8\" font-family=\"Times,serif\" font-size=\"14.00\">grad 0.5000</text>\n",
+       "</g>\n",
+       "<!-- 140307056976704+ -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>140307056976704+</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1008\" cy=\"-72.5\" rx=\"27\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1008\" y=\"-68.8\" font-family=\"Times,serif\" font-size=\"14.00\">+</text>\n",
+       "</g>\n",
+       "<!-- 140307056978624&#45;&gt;140307056976704+ -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>140307056978624&#45;&gt;140307056976704+</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"971.05,-71.12 981.43,-69.01 972,-64.18 971.05,-71.12\"/>\n",
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+       "<!-- 140307056977616 -->\n",
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+       "<text text-anchor=\"middle\" x=\"725\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">x1*w1 + x2*w2</text>\n",
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+       "<text text-anchor=\"middle\" x=\"820.5\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">data &#45;6.0000</text>\n",
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+       "</g>\n",
+       "<!-- 140307056977616&#45;&gt;140307056976704+ -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>140307056977616&#45;&gt;140307056976704+</title>\n",
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+       "</g>\n",
+       "<!-- 140307056977616+&#45;&gt;140307056977616 -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>140307056977616+&#45;&gt;140307056977616</title>\n",
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+       "</g>\n",
+       "<!-- 140307056976608&#45;&gt;140307056977616+ -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>140307056976608&#45;&gt;140307056977616+</title>\n",
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+       "<title>140307056976608*&#45;&gt;140307056976608</title>\n",
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+       "</g>\n",
+       "<!-- 140307056976704&#45;&gt;140307056976512tanh -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>140307056976704&#45;&gt;140307056976512tanh</title>\n",
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+       "</g>\n",
+       "<!-- 140307056976704+&#45;&gt;140307056976704 -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>140307056976704+&#45;&gt;140307056976704</title>\n",
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+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x7f9bc8477bb0>"
+      ]
+     },
+     "execution_count": 244,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "draw_dot(o)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "o.backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Value(data=6.881373587019543),\n",
+       " Value(data=2.0),\n",
+       " Value(data=-3.0),\n",
+       " Value(data=-6.0),\n",
+       " Value(data=0.0),\n",
+       " Value(data=1.0),\n",
+       " Value(data=0.0),\n",
+       " Value(data=-6.0),\n",
+       " Value(data=0.8813735870195432),\n",
+       " Value(data=0.7071067811865476)]"
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "topo = []\n",
+    "visited = set()\n",
+    "def build_topo(v):\n",
+    "  if v not in visited:\n",
+    "    visited.add(v)\n",
+    "    for child in v._prev:\n",
+    "      build_topo(child)\n",
+    "    topo.append(v)\n",
+    "build_topo(o)\n",
+    "topo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "o.grad = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "o._backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n._backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b._backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1w1x2w2._backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x2w2._backward()\n",
+    "x1w1._backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1.grad = w1.data * x1w1.grad\n",
+    "w1.grad = x1.data * x1w1.grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x2.grad = w2.data * x2w2.grad\n",
+    "w2.grad = x2.data * x2w2.grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1w1.grad = 0.5\n",
+    "x2w2.grad = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1w1x2w2.grad = 0.5\n",
+    "b.grad = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.grad = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "o.grad = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4999999999999999"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 - o.data**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# o = tanh(n)\n",
+    "# do/dn = 1 - o**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {},
+   "outputs": [
+    {
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+    "a = Value(3.0, label='a')\n",
+    "b = a + a   ; b.label = 'b'\n",
+    "b.backward()\n",
+    "draw_dot(b)"
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+   "metadata": {},
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+     "execution_count": 259,
+     "metadata": {},
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+    }
+   ],
+   "source": [
+    "a = Value(-2.0, label='a')\n",
+    "b = Value(3.0, label='b')\n",
+    "d = a * b    ; d.label = 'd'\n",
+    "e = a + b    ; e.label = 'e'\n",
+    "f = d * e    ; f.label = 'f'\n",
+    "\n",
+    "f.backward()\n",
+    "\n",
+    "draw_dot(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
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+}
diff --git a/lectures/micrograd/.ipynb_checkpoints/micrograd_lecture_second_half_roughly-checkpoint.ipynb b/lectures/micrograd/.ipynb_checkpoints/micrograd_lecture_second_half_roughly-checkpoint.ipynb
new file mode 100644
index 00000000..5e1a233a
--- /dev/null
+++ b/lectures/micrograd/.ipynb_checkpoints/micrograd_lecture_second_half_roughly-checkpoint.ipynb
@@ -0,0 +1,4268 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(x):\n",
+    "  return 3*x**2 - 4*x + 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(3.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f9b98432ee0>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xs = np.arange(-5, 5, 0.25)\n",
+    "ys = f(xs)\n",
+    "plt.plot(xs, ys)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.999378523327323e-06"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h = 0.000001\n",
+    "x = 2/3\n",
+    "(f(x + h) - f(x))/h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# les get more complex\n",
+    "a = 2.0\n",
+    "b = -3.0\n",
+    "c = 10.0\n",
+    "d = a*b + c\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "d1 4.0\n",
+      "d2 4.0001\n",
+      "slope 0.9999999999976694\n"
+     ]
+    }
+   ],
+   "source": [
+    "h = 0.0001\n",
+    "\n",
+    "# inputs\n",
+    "a = 2.0\n",
+    "b = -3.0\n",
+    "c = 10.0\n",
+    "\n",
+    "d1 = a*b + c\n",
+    "c += h\n",
+    "d2 = a*b + c\n",
+    "\n",
+    "print('d1', d1)\n",
+    "print('d2', d2)\n",
+    "print('slope', (d2 - d1)/h)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Value:\n",
+    "  \n",
+    "  def __init__(self, data, _children=(), _op='', label=''):\n",
+    "    self.data = data\n",
+    "    self.grad = 0.0\n",
+    "    self._backward = lambda: None\n",
+    "    self._prev = set(_children)\n",
+    "    self._op = _op\n",
+    "    self.label = label\n",
+    "\n",
+    "  def __repr__(self):\n",
+    "    return f\"Value(data={self.data})\"\n",
+    "  \n",
+    "  def __add__(self, other):\n",
+    "    other = other if isinstance(other, Value) else Value(other)\n",
+    "    out = Value(self.data + other.data, (self, other), '+')\n",
+    "    \n",
+    "    def _backward():\n",
+    "      self.grad += 1.0 * out.grad\n",
+    "      other.grad += 1.0 * out.grad\n",
+    "    out._backward = _backward\n",
+    "    \n",
+    "    return out\n",
+    "\n",
+    "  def __mul__(self, other):\n",
+    "    other = other if isinstance(other, Value) else Value(other)\n",
+    "    out = Value(self.data * other.data, (self, other), '*')\n",
+    "    \n",
+    "    def _backward():\n",
+    "      self.grad += other.data * out.grad\n",
+    "      other.grad += self.data * out.grad\n",
+    "    out._backward = _backward\n",
+    "      \n",
+    "    return out\n",
+    "  \n",
+    "  def __pow__(self, other):\n",
+    "    assert isinstance(other, (int, float)), \"only supporting int/float powers for now\"\n",
+    "    out = Value(self.data**other, (self,), f'**{other}')\n",
+    "\n",
+    "    def _backward():\n",
+    "        self.grad += other * (self.data ** (other - 1)) * out.grad\n",
+    "    out._backward = _backward\n",
+    "\n",
+    "    return out\n",
+    "  \n",
+    "  def __rmul__(self, other): # other * self\n",
+    "    return self * other\n",
+    "\n",
+    "  def __truediv__(self, other): # self / other\n",
+    "    return self * other**-1\n",
+    "\n",
+    "  def __neg__(self): # -self\n",
+    "    return self * -1\n",
+    "\n",
+    "  def __sub__(self, other): # self - other\n",
+    "    return self + (-other)\n",
+    "\n",
+    "  def __rsub__(self, other): # other - self\n",
+    "    return self - other\n",
+    "\n",
+    "  def __radd__(self, other): # other + self\n",
+    "    return self + other\n",
+    "\n",
+    "  def tanh(self):\n",
+    "    x = self.data\n",
+    "    t = (math.exp(2*x) - 1)/(math.exp(2*x) + 1)\n",
+    "    out = Value(t, (self, ), 'tanh')\n",
+    "    \n",
+    "    def _backward():\n",
+    "      self.grad += (1 - t**2) * out.grad\n",
+    "    out._backward = _backward\n",
+    "    \n",
+    "    return out\n",
+    "  \n",
+    "  def exp(self):\n",
+    "    x = self.data\n",
+    "    out = Value(math.exp(x), (self, ), 'exp')\n",
+    "    \n",
+    "    def _backward():\n",
+    "      self.grad += out.data * out.grad # NOTE: in the video I incorrectly used = instead of +=. Fixed here.\n",
+    "    out._backward = _backward\n",
+    "    \n",
+    "    return out\n",
+    "  \n",
+    "  \n",
+    "  def backward(self):\n",
+    "    \n",
+    "    topo = []\n",
+    "    visited = set()\n",
+    "    def build_topo(v):\n",
+    "      if v not in visited:\n",
+    "        visited.add(v)\n",
+    "        for child in v._prev:\n",
+    "          build_topo(child)\n",
+    "        topo.append(v)\n",
+    "    build_topo(self)\n",
+    "    \n",
+    "    self.grad = 1.0\n",
+    "    for node in reversed(topo):\n",
+    "      node._backward()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphviz import Digraph\n",
+    "\n",
+    "def trace(root):\n",
+    "  # builds a set of all nodes and edges in a graph\n",
+    "  nodes, edges = set(), set()\n",
+    "  def build(v):\n",
+    "    if v not in nodes:\n",
+    "      nodes.add(v)\n",
+    "      for child in v._prev:\n",
+    "        edges.add((child, v))\n",
+    "        build(child)\n",
+    "  build(root)\n",
+    "  return nodes, edges\n",
+    "\n",
+    "def draw_dot(root):\n",
+    "  dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n",
+    "  \n",
+    "  nodes, edges = trace(root)\n",
+    "  for n in nodes:\n",
+    "    uid = str(id(n))\n",
+    "    # for any value in the graph, create a rectangular ('record') node for it\n",
+    "    dot.node(name = uid, label = \"{ %s | data %.4f | grad %.4f }\" % (n.label, n.data, n.grad), shape='record')\n",
+    "    if n._op:\n",
+    "      # if this value is a result of some operation, create an op node for it\n",
+    "      dot.node(name = uid + n._op, label = n._op)\n",
+    "      # and connect this node to it\n",
+    "      dot.edge(uid + n._op, uid)\n",
+    "\n",
+    "  for n1, n2 in edges:\n",
+    "    # connect n1 to the op node of n2\n",
+    "    dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n",
+    "\n",
+    "  return dot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inputs x1,x2\n",
+    "x1 = Value(2.0, label='x1')\n",
+    "x2 = Value(0.0, label='x2')\n",
+    "# weights w1,w2\n",
+    "w1 = Value(-3.0, label='w1')\n",
+    "w2 = Value(1.0, label='w2')\n",
+    "# bias of the neuron\n",
+    "b = Value(6.8813735870195432, label='b')\n",
+    "# x1*w1 + x2*w2 + b\n",
+    "x1w1 = x1*w1; x1w1.label = 'x1*w1'\n",
+    "x2w2 = x2*w2; x2w2.label = 'x2*w2'\n",
+    "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = 'x1*w1 + x2*w2'\n",
+    "n = x1w1x2w2 + b; n.label = 'n'\n",
+    "o = n.tanh(); o.label = 'o'\n",
+    "o.backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {},
+   "outputs": [
+    {
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+     "execution_count": 320,
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+    }
+   ],
+   "source": [
+    "# inputs x1,x2\n",
+    "x1 = Value(2.0, label='x1')\n",
+    "x2 = Value(0.0, label='x2')\n",
+    "# weights w1,w2\n",
+    "w1 = Value(-3.0, label='w1')\n",
+    "w2 = Value(1.0, label='w2')\n",
+    "# bias of the neuron\n",
+    "b = Value(6.8813735870195432, label='b')\n",
+    "# x1*w1 + x2*w2 + b\n",
+    "x1w1 = x1*w1; x1w1.label = 'x1*w1'\n",
+    "x2w2 = x2*w2; x2w2.label = 'x2*w2'\n",
+    "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = 'x1*w1 + x2*w2'\n",
+    "n = x1w1x2w2 + b; n.label = 'n'\n",
+    "# ----\n",
+    "e = (2*n).exp()\n",
+    "o = (e - 1) / (e + 1)\n",
+    "# ----\n",
+    "o.label = 'o'\n",
+    "o.backward()\n",
+    "draw_dot(o)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7071066904050358\n",
+      "---\n",
+      "x2 0.5000001283844369\n",
+      "w2 0.0\n",
+      "x1 -1.5000003851533106\n",
+      "w1 1.0000002567688737\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "x1 = torch.Tensor([2.0]).double()                ; x1.requires_grad = True\n",
+    "x2 = torch.Tensor([0.0]).double()                ; x2.requires_grad = True\n",
+    "w1 = torch.Tensor([-3.0]).double()               ; w1.requires_grad = True\n",
+    "w2 = torch.Tensor([1.0]).double()                ; w2.requires_grad = True\n",
+    "b = torch.Tensor([6.8813735870195432]).double()  ; b.requires_grad = True\n",
+    "n = x1*w1 + x2*w2 + b\n",
+    "o = torch.tanh(n)\n",
+    "\n",
+    "print(o.data.item())\n",
+    "o.backward()\n",
+    "\n",
+    "print('---')\n",
+    "print('x2', x2.grad.item())\n",
+    "print('w2', w2.grad.item())\n",
+    "print('x1', x1.grad.item())\n",
+    "print('w1', w1.grad.item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class Neuron:\n",
+    "  \n",
+    "  def __init__(self, nin):\n",
+    "    self.w = [Value(random.uniform(-1,1)) for _ in range(nin)]\n",
+    "    self.b = Value(random.uniform(-1,1))\n",
+    "  \n",
+    "  def __call__(self, x):\n",
+    "    # w * x + b\n",
+    "    act = sum((wi*xi for wi, xi in zip(self.w, x)), self.b)\n",
+    "    out = act.tanh()\n",
+    "    return out\n",
+    "  \n",
+    "  def parameters(self):\n",
+    "    return self.w + [self.b]\n",
+    "\n",
+    "class Layer:\n",
+    "  \n",
+    "  def __init__(self, nin, nout):\n",
+    "    self.neurons = [Neuron(nin) for _ in range(nout)]\n",
+    "  \n",
+    "  def __call__(self, x):\n",
+    "    outs = [n(x) for n in self.neurons]\n",
+    "    return outs[0] if len(outs) == 1 else outs\n",
+    "  \n",
+    "  def parameters(self):\n",
+    "    return [p for neuron in self.neurons for p in neuron.parameters()]\n",
+    "\n",
+    "class MLP:\n",
+    "  \n",
+    "  def __init__(self, nin, nouts):\n",
+    "    sz = [nin] + nouts\n",
+    "    self.layers = [Layer(sz[i], sz[i+1]) for i in range(len(nouts))]\n",
+    "  \n",
+    "  def __call__(self, x):\n",
+    "    for layer in self.layers:\n",
+    "      x = layer(x)\n",
+    "    return x\n",
+    "  \n",
+    "  def parameters(self):\n",
+    "    return [p for layer in self.layers for p in layer.parameters()]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Value(data=0.7386423044748194)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = [2.0, 3.0, -1.0]\n",
+    "n = MLP(3, [4, 4, 1])\n",
+    "n(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
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+    "draw_dot(n(x))"
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+   "execution_count": 666,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs = [\n",
+    "  [2.0, 3.0, -1.0],\n",
+    "  [3.0, -1.0, 0.5],\n",
+    "  [0.5, 1.0, 1.0],\n",
+    "  [1.0, 1.0, -1.0],\n",
+    "]\n",
+    "ys = [1.0, -1.0, -1.0, 1.0] # desired targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 682,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 0.002056123958292787\n",
+      "1 0.0020404768419831024\n",
+      "2 0.0020250564320649566\n",
+      "3 0.002009857924015696\n",
+      "4 0.001994876646733686\n",
+      "5 0.0019801080579702683\n",
+      "6 0.001965547739947282\n",
+      "7 0.0019511913951512907\n",
+      "8 0.0019370348422964524\n",
+      "9 0.0019230740124479978\n",
+      "10 0.001909304945299319\n",
+      "11 0.0018957237855951486\n",
+      "12 0.0018823267796946328\n",
+      "13 0.0018691102722676993\n",
+      "14 0.0018560707031189828\n",
+      "15 0.0018432046041333716\n",
+      "16 0.0018305085963379896\n",
+      "17 0.0018179793870754363\n",
+      "18 0.0018056137672833098\n",
+      "19 0.0017934086088756394\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "for k in range(20):\n",
+    "  \n",
+    "  # forward pass\n",
+    "  ypred = [n(x) for x in xs]\n",
+    "  loss = sum((yout - ygt)**2 for ygt, yout in zip(ys, ypred))\n",
+    "  \n",
+    "  # backward pass\n",
+    "  for p in n.parameters():\n",
+    "    p.grad = 0.0\n",
+    "  loss.backward()\n",
+    "  \n",
+    "  # update\n",
+    "  for p in n.parameters():\n",
+    "    p.data += -0.1 * p.grad\n",
+    "  \n",
+    "  print(k, loss.data)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 683,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Value(data=0.9817830812439714),\n",
+       " Value(data=-0.9863881624765284),\n",
+       " Value(data=-0.9766534529377958),\n",
+       " Value(data=0.9729591216966093)]"
+      ]
+     },
+     "execution_count": 683,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ypred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lectures/micrograd/micrograd_lecture_first_half_roughly.ipynb b/lectures/micrograd/micrograd_lecture_first_half_roughly.ipynb
index 1a3fa629..6a7b3529 100644
--- a/lectures/micrograd/micrograd_lecture_first_half_roughly.ipynb
+++ b/lectures/micrograd/micrograd_lecture_first_half_roughly.ipynb
@@ -1256,7 +1256,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1270,7 +1270,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.10"
   }
  },
  "nbformat": 4,
diff --git a/lectures/micrograd/micrograd_lecture_second_half_roughly.ipynb b/lectures/micrograd/micrograd_lecture_second_half_roughly.ipynb
index 67a25a68..5e1a233a 100644
--- a/lectures/micrograd/micrograd_lecture_second_half_roughly.ipynb
+++ b/lectures/micrograd/micrograd_lecture_second_half_roughly.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 382,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 328,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,6 +216,9 @@
     "  def __sub__(self, other): # self - other\n",
     "    return self + (-other)\n",
     "\n",
+    "  def __rsub__(self, other): # other - self\n",
+    "    return self - other\n",
+    "\n",
     "  def __radd__(self, other): # other + self\n",
     "    return self + other\n",
     "\n",
@@ -261,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1081,7 +1084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 592,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1130,16 +1133,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 665,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Value(data=0.16578526021381612)"
+       "Value(data=0.7386423044748194)"
       ]
      },
-     "execution_count": 665,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1150,6 +1153,2998 @@
     "n(x)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
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