Add Supplementary links from YouTube descriptions to README

README.md

@@ -23,6 +23,11 @@ We implement a bigram character-level language model, which we will further comp
 - [Jupyter notebook files](lectures/makemore/makemore_part1_bigrams.ipynb)
 - [makemore Github repo](https://github.com/karpathy/makemore)
 
+**Supplementary links**
+
+* [Python + Numpy tutorial from CS231n](https://cs231n.github.io/python-numpy-tutorial/)
+* [PyTorch tutorial on Tensors](https://pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html)
+* [Introduction to PyTorch](https://pytorch.org/tutorials/beginner/nlp/pytorch_tutorial.html)
 ---
 
 **Lecture 3: Building makemore Part 2: MLP**
@@ -33,6 +38,11 @@ We implement a multilayer perceptron (MLP) character-level language model. In th
 - [Jupyter notebook files](lectures/makemore/makemore_part2_mlp.ipynb)
 - [makemore Github repo](https://github.com/karpathy/makemore)
 
+**Supplementary links**
+
+* [A Neural Probabilistic Language Model](https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)
+* A blog post about [PyTorch Internals](http://blog.ezyang.com/2019/05/pytorch-internals/)
+
 ---
 
 **Lecture 4: Building makemore Part 3: Activations & Gradients, BatchNorm**
@@ -43,6 +53,13 @@ We dive into some of the internals of MLPs with multiple layers and scrutinize t
 - [Jupyter notebook files](lectures/makemore/makemore_part3_bn.ipynb)
 - [makemore Github repo](https://github.com/karpathy/makemore)
 
+**Supplementary links**
+
+* [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://arxiv.org/abs/1502.01852)
+* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
+* [A Neural Probabilistic Language Model](https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)
+* [Rethinking "Batch" in BatchNorm](https://arxiv.org/abs/2105.07576)
+
 ---
 
 **Lecture 5: Building makemore Part 4: Becoming a Backprop Ninja**
@@ -55,6 +72,13 @@ I recommend you work through the exercise yourself but work with it in tandem an
 - [Jupyter notebook files](lectures/makemore/makemore_part4_backprop.ipynb)
 - [makemore Github repo](https://github.com/karpathy/makemore)
 
+**Supplementary links**
+
+* [Yes you should understand backprop](https://karpathy.medium.com/yes-you-should-understand-backprop-e2f06eab496b) (blog post)
+* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
+* [Bessel's Correction](https://mathcenter.oxford.emory.edu/site/math117/besselCorrection/)
+* [A Neural Probabilistic Language Model](https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)
+
 ---
 
 **Lecture 6: Building makemore Part 5: Building WaveNet**
@@ -64,6 +88,11 @@ We take the 2-layer MLP from previous video and make it deeper with a tree-like
 - [YouTube video lecture](https://youtu.be/t3YJ5hKiMQ0)
 - [Jupyter notebook files](lectures/makemore/makemore_part5_cnn1.ipynb)
 
+**Supplementary links**
+
+* [WaveNet: A Generative Model for Raw Audio](https://arxiv.org/abs/1609.03499)
+* [A Neural Probabilistic Language Model](https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)
+
 ---
 
 
@@ -73,10 +102,16 @@ We build a Generatively Pretrained Transformer (GPT), following the paper "Atten
 
 - [YouTube video lecture](https://www.youtube.com/watch?v=kCc8FmEb1nY). For all other links see the video description.
 
+**Supplementary links**
+
+* [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
+* [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
+* [Introducing ChatGPT](https://openai.com/blog/chatgpt) (blog post)
+
 ---
 
 Ongoing...
 
 **License**
 
-MIT
+MIT