jina-ai · scott-martens · May 2, 2023 · May 2, 2023 · May 2, 2023 · May 2, 2023
diff --git a/README.md b/README.md
@@ -18,12 +18,12 @@
 
 <!-- start elevator-pitch -->
 
-Fine-tuning is an effective way to improve performance on [neural search](https://jina.ai/news/what-is-neural-search-and-learn-to-build-a-neural-search-engine/) tasks.
+Fine-tuning is an effective way to improve AI model performance on specific tasks and use cases tasks.
 However, setting up and performing fine-tuning can be very time-consuming and resource-intensive.
 
 Jina AI's Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud.
 With Finetuner, you can easily enhance the performance of pre-trained models,
-making them production-ready [without extensive labeling](https://jina.ai/news/fine-tuning-with-low-budget-and-high-expectations/) or expensive hardware.
+making them production-ready [with less data](https://jina.ai/news/fine-tuning-with-low-budget-and-high-expectations/) and without investing in expensive hardware.
 
 🎏 **Better embeddings**: Create high-quality embeddings for semantic search, visual similarity search, cross-modal text<->image search, recommendation systems,
 clustering, duplication detection, anomaly detection, or other uses.
@@ -39,6 +39,7 @@ freezing, dimensionality reduction, hard-negative mining, cross-modal models, an
 ☁ **All-in-cloud**: Train using our GPU infrastructure, manage runs, experiments, and artifacts on Jina AI Cloud
 without worrying about resource availability, complex integration, or infrastructure costs.
 
+
 <!-- end elevator-pitch -->
 
 ## [Documentation](https://finetuner.jina.ai/)

diff --git a/docs/dummy/dummy.md b/docs/dummy/dummy.md
@@ -0,0 +1,16 @@
+![The Mona Lisa with a section cut out.](../imgs/MonaLisa1.png)
+![The Mona Lisa with added blurring.](../imgs/MonaLisa2.png) 
+![2D space](../imgs/xy-axes.png)
+![3D space](../imgs/xyz-axes.png)
+![Vector notation](../imgs/notation1.png)
+![Vector notation](../imgs/notation2.png)
+![Vector notation](../imgs/notation3.png)
+![Vector notation](../imgs/notation4.png)
+![Pythagoras Theorem](../imgs/PythagorasTheorem.png)
+![Algebra](../imgs/algebra1.png)
+![Algebra](../imgs/algebra2.png)
+![Algebra](../imgs/algebra3.png)
+![Algebra](../imgs/algebra4.png)
+![Cosine](../imgs/Cosine.png)
+![Cosine Formula](../imgs/Cosine1.png)
+![Cosine Formula](../imgs/Cosine2.png)
diff --git a/docs/imgs/Cosine.png b/docs/imgs/Cosine.png
diff --git a/docs/imgs/Cosine1.png b/docs/imgs/Cosine1.png
diff --git a/docs/imgs/Cosine2.png b/docs/imgs/Cosine2.png
diff --git a/docs/imgs/Dont_Panic.png b/docs/imgs/Dont_Panic.png
diff --git a/docs/imgs/Faces.png b/docs/imgs/Faces.png
diff --git a/docs/imgs/HairType.png b/docs/imgs/HairType.png
diff --git a/docs/imgs/MenWomen.png b/docs/imgs/MenWomen.png
diff --git a/docs/imgs/MonaLisa1.png b/docs/imgs/MonaLisa1.png
diff --git a/docs/imgs/MonaLisa2.png b/docs/imgs/MonaLisa2.png
diff --git a/docs/imgs/PythagorasTheorem.png b/docs/imgs/PythagorasTheorem.png
diff --git a/docs/imgs/Wynona.png b/docs/imgs/Wynona.png
diff --git a/docs/imgs/algebra1.png b/docs/imgs/algebra1.png
diff --git a/docs/imgs/algebra2.png b/docs/imgs/algebra2.png
diff --git a/docs/imgs/algebra3.png b/docs/imgs/algebra3.png
diff --git a/docs/imgs/algebra4.png b/docs/imgs/algebra4.png
diff --git a/docs/imgs/dog_w_text.png b/docs/imgs/dog_w_text.png
diff --git a/docs/imgs/finetuning-adjusting-dials.png b/docs/imgs/finetuning-adjusting-dials.png
diff --git a/docs/imgs/fruit_NN2.png b/docs/imgs/fruit_NN2.png
diff --git a/docs/imgs/fruit_and_dots.png b/docs/imgs/fruit_and_dots.png
diff --git a/docs/imgs/matrix1.png b/docs/imgs/matrix1.png
diff --git a/docs/imgs/matrix2.png b/docs/imgs/matrix2.png
diff --git a/docs/imgs/matrix3.png b/docs/imgs/matrix3.png
diff --git a/docs/imgs/matrix_eq1.png b/docs/imgs/matrix_eq1.png
diff --git a/docs/imgs/nn_first_layer.png b/docs/imgs/nn_first_layer.png
diff --git a/docs/imgs/nn_small.png b/docs/imgs/nn_small.png
diff --git a/docs/imgs/notation1.png b/docs/imgs/notation1.png
diff --git a/docs/imgs/notation2.png b/docs/imgs/notation2.png
diff --git a/docs/imgs/notation3.png b/docs/imgs/notation3.png
diff --git a/docs/imgs/notation4.png b/docs/imgs/notation4.png
diff --git a/docs/imgs/ordered_veg.png b/docs/imgs/ordered_veg.png
diff --git a/docs/imgs/plain_fruit_NN.png b/docs/imgs/plain_fruit_NN.png
diff --git a/docs/imgs/relu.png b/docs/imgs/relu.png
diff --git a/docs/imgs/relu1.png b/docs/imgs/relu1.png
diff --git a/docs/imgs/scatterplot_fruit.png b/docs/imgs/scatterplot_fruit.png
diff --git a/docs/imgs/scatterplot_fruit_ordered.png b/docs/imgs/scatterplot_fruit_ordered.png
diff --git a/docs/imgs/scatterplot_fruit_partitioned.png b/docs/imgs/scatterplot_fruit_partitioned.png
diff --git a/docs/imgs/scatterplot_veg.png b/docs/imgs/scatterplot_veg.png
diff --git a/docs/imgs/sigmoid.png b/docs/imgs/sigmoid.png
diff --git a/docs/imgs/thresholds.png b/docs/imgs/thresholds.png
diff --git a/docs/imgs/thresholds1.png b/docs/imgs/thresholds1.png
diff --git a/docs/imgs/tomato_centroid.png b/docs/imgs/tomato_centroid.png
diff --git a/docs/imgs/tomato_centroid_arrow.png b/docs/imgs/tomato_centroid_arrow.png
diff --git a/docs/imgs/unordered_veg_partition.png b/docs/imgs/unordered_veg_partition.png
diff --git a/docs/imgs/unordered_veg_partition_softmax.png b/docs/imgs/unordered_veg_partition_softmax.png
diff --git a/docs/imgs/vector.png b/docs/imgs/vector.png
diff --git a/docs/imgs/veg_NN.jpg b/docs/imgs/veg_NN.jpg
diff --git a/docs/imgs/veg_apart.png b/docs/imgs/veg_apart.png
diff --git a/docs/imgs/veg_together.png b/docs/imgs/veg_together.png
diff --git a/docs/imgs/veg_triplet.png b/docs/imgs/veg_triplet.png
diff --git a/docs/imgs/xy-axes.png b/docs/imgs/xy-axes.png
diff --git a/docs/imgs/xyz-axes.png b/docs/imgs/xyz-axes.png
diff --git a/docs/intro/AI-pipeline.md b/docs/intro/AI-pipeline.md
@@ -0,0 +1,3 @@
+In practice, we typically do transform the input data in some way before inputting
+it into a neural network (we call this *preprocessing*) and have to perform some
+transformation on the output. Nonetheless, 
diff --git a/docs/intro/brief-refresher-on-vectors.md b/docs/intro/brief-refresher-on-vectors.md
@@ -0,0 +1,115 @@
+# {octicon}`light-bulb` Brief Refresher on Vectors
+
+Vectors are ordered lists of numbers that correspond to points in a high-dimensional 
+metric space. For example, a point on a graph is defined by a vector. In the 
+image below, you can see three points on a graph, each defined by a vector: 
+`[-3,1]`, `[4,2]`, `[2.5,-3]`
+
+![Untitled](../imgs/xy-axes.png)
+
+Points in a two-dimensional space are defined by vectors of two numbers. In a 
+three-dimensional space, you need vectors with three numbers. In the image below, 
+point *Q* is defined by the vector `[-5, -5, 7]` and point *P* by `[3,0,5]`:
+
+![Untitled](../imgs/xyz-axes.png)
+
+A vector isn’t limited to three numbers. It can have any amount — hundreds, 
+thousands, even millions or billions — defining a point in what’s called a 
+*vector space*. 
+
+These vector spaces are just extensions of the three-dimensional space we are 
+all used to. It can be hard to imagine a space with millions of dimensions, and 
+even harder to draw a picture of it, but they still have the same properties 
+two- and three-dimensional spaces have: Every point is uniquely defined by a 
+vector and each vector defines a unique point. If two vectors are the same, 
+they correspond to the same point in space, and the distance between them is 
+therefore zero. Any two points that correspond to different vectors are different 
+points, and we can calculate the distance between them from their vectors.
+
+## Vector Notation
+
+Traditionally, we write vectors with a little arrow above them, like this 
+five-dimensional vector:
+
+![Untitled](../imgs/notation1.png)
+
+Or this one:
+
+![Untitled](../imgs/notation2.png)
+
+And we designate the individual numbers in a vector with a subscript:
+
+![Untitled](../imgs/notation3.png)
+
+![Untitled](../imgs/notation4.png)
+
+## Distance
+
+There are a number of different kinds of distances that we can calculate 
+between vectors. The two most commonly used in neural networks are 
+*Euclidean distance*, which closely corresponds to our common-sense ideas 
+about distance, and *cosine distance*, which measures the angle between 
+vectors as if they were lines from the origin, instead of points.
+
+### Euclidean distance
+
+Euclidean distance corresponds closely with our everyday sense of distance. 
+If a town is two kilometers to the north and four kilometers to the east, 
+then the straight-line distance to that town is five kilometers, because of 
+the Pythagorean theorem:
+
+![Pythagoras2.png](../imgs/PythagorasTheorem.png)
+
+If sides *a* and *b* form a right angle (= 90 degrees), then the length of 
+side *c* must fit the equation:
+
+[//]: # (![Untitled]&#40;../imgs/algebra1.png&#41;)
+<p align="left"><img src="../../_images/algebra1.png"/></p>
+
+This means:
+
+[//]: # (![Untitled]&#40;../imgs/algebra2.png&#41;)
+<p align="left"><img src="../../_images/algebra2.png"/></p>
+
+We can generalize this to get the Euclidean distance between any two 
+*n*-dimensional vectors with the formula:
+
+[//]: # (![Untitled]&#40;../imgs/algebra3.png&#41;)
+<p align="left"><img src="../../_images/algebra3.png"/></p>
+
+For two five-dimensional vectors, `[2 -3 7 4 1]` and `[-7 0 -2 5 4]`, 
+it looks like this:
+
+[//]: # (![Untitled]&#40;../imgs/algebra4.png&#41;)
+<p align="left"><img src="../../_images/algebra4.png"/></p>
+
+Even if two vectors have thousands or millions of dimensions, it’s 
+pretty trivial for a computer to calculate the Euclidean distance 
+between them.
+
+### Cosine distance
+
+Vectors can also be seen as lines in a high-dimensional space from the 
+origin (the point where all the numbers in the vector are zero) to the 
+point designated by the coordinates in the vector.
+
+![Cosine.png](../imgs/Cosine.png)
+
+Seen in this way, we can calculate the *angle* between the two vectors. 
+For two vectors of dimension *n*:
+
+[//]: # (![Untitled]&#40;../imgs/Cosine1.png&#41;)
+<p align="left"><img src="../../_images/Cosine1.png"/></p>
+
+For the two-dimensional vectors `[2,3]` and `[5,1]`:
+
+[//]: # (![Untitled]&#40;../imgs/Cosine2.png&#41;)
+<p align="left"><img src="../../_images/Cosine2.png"/></p>
+
+Usually, we just stick to the cosine, without calculating the radians or 
+degrees of the angle. If *cos(θ) = 1*, then the angle *θ* is zero degrees, 
+if *cos(θ) = 0* then the angle *θ* is 90 degrees.
+
+We sometimes use other measures than Euclidean distance and cosine, but 
+these are the two most important ones, and you can see how both are quickly 
+calculated from vectors.