📚 docs: change project description and add links (#57)

CHANGELOG.md

@@ -8,7 +8,7 @@ All notable changes to this project will be documented in this file.
 
 ### Features
 
-🪜 **feat:** Softmax1D, DotProduct1D & Constant1D ([#49](https://github.com/owkin/GrAIdient/pull/49))\
+🪜 **layer_1d:** Softmax1D, DotProduct1D & Constant1D ([#49](https://github.com/owkin/GrAIdient/pull/49))\
 🪜 **feat:** remove activation from layer ([#47](https://github.com/owkin/GrAIdient/pull/47))\
 🪜 **feat:** LayerMerge1D, Sum1D, Concat1D, Concat2D ([#43](https://github.com/owkin/GrAIdient/pull/43))\
 🪜 **layer_2d:** Deconvolution2D ([#42](https://github.com/owkin/GrAIdient/pull/42))\
@@ -22,6 +22,7 @@ All notable changes to this project will be documented in this file.
 
 ### Miscellaneous Tasks
 
+📚 **docs:** change project description and add links ([#57](https://github.com/owkin/GrAIdient/pull/57))\
 📚 **docs:** PropertyListEncoder by default ([#51](https://github.com/owkin/GrAIdient/pull/51))\
 🎉 **refactor:** logo ([#46](https://github.com/owkin/GrAIdient/pull/46))\
 🎉 **refactor!:** re brand the framework ([#40](https://github.com/owkin/GrAIdient/pull/40))

Docs/Architecture/GrAIExamples.md

@@ -1,5 +1,6 @@
 # 👷 GrAIExamples
 
-`GrAIExamples` contains examples that show how to interact with `GrAIdient`. 
+[`GrAIExamples`](../../Tests/GrAIExamples) contains examples 
+that show how to interact with `GrAIdient`. 
 
 See the [examples](../Examples/EXAMPLES.md) documentation for more details.

Docs/Architecture/GrAITests.md

@@ -1,6 +1,6 @@
 # 👷 GrAITests
 
-`GrAITests` contains unit tests of `GrAIdient`. 
+[`GrAITests`](../../Tests/GrAITests) contains unit tests of `GrAIdient`. 
 
 Some of them are systematic and repeated over many different setups to ensure 
 that every layer, optimizer, activation function ... is tested.

Docs/Architecture/GrAITestsUtils.md

@@ -1,5 +1,6 @@
 # 👷 GrAITestsUtils
 
-`GrAITestsUtils` contains utils to enable the testing of plugins of `GrAIdient`.
+[`GrAITestsUtils`](../../Sources/GrAITestsUtils) contains utils 
+to enable the testing of plugins of `GrAIdient`.
 
 To know more about plugins, see [this documentation](../Concepts/PLUGIN.md).

Docs/Architecture/GrAITorchTests.md

@@ -1,7 +1,7 @@
 # 👷 GrAITorchTests
 
-`GrAITorchTests` contains integration tests that allow to compare `GrAIdient` models 
-with their equivalent in `PyTorch`.
+[`GrAITorchTests`](../../Tests/GrAITorchTests) contains integration tests 
+that allow to compare `GrAIdient` models with their equivalent in `PyTorch`.
 
 The goal is to demonstrate a good level of reproducibility and 
 interoperability with `PyTorch`.

Docs/Architecture/GrAIdient.md

@@ -1,6 +1,6 @@
 # 👷 GrAIdient
 
-`GrAIdient` contains the different API needed to train models and interact 
-with them.
+[`GrAIdient`](../../Sources/GrAIdient) contains the different API needed 
+to train models and interact with them.
 
 See the [main concepts](../Concepts/CONCEPTS.md) to know more about `GrAIdient`.

Docs/Examples/EXAMPLES.md

@@ -1,6 +1,7 @@
 # 🚀 Examples
 
-`GrAIExamples` contains examples that show how to interact with `GrAIdient`. 
+[`GrAIExamples`](../../Tests/GrAIExamples) contains examples 
+that show how to interact with `GrAIdient`. 
 The examples are organised as tests so as to demonstrate some capabilities. 
 They may be run in local (Xcode or command line) 
 or in the [GitHub](https://github.com/owkin/GrAIdient/actions) CI 

Docs/Examples/VGG.md

@@ -1,9 +1,11 @@
 # 🚀 VGG Example
 
-This example trains a simple model (simplified from the VGG architecture) 
-on the GPU. The dataset used is CIFAR 10. 
+This is the documentation of a [toy VGG model](../../Tests/GrAIExamples/VGGExample.swift) 
+(simplified from the VGG architecture), trained on the GPU. 
+The dataset used is CIFAR 10. 
+
 We want to train the model to discriminate between 2 labels 
-(not the 10 available): the 8 (ships) or the 5 (dogs).
+(not the 10 available of the CIFAR 10 dataset): the 8 (ships) or the 5 (dogs).
 
 Here is a subset of images we find for the label 8 (ships) vs label 5 (dogs).
 

README.md

@@ -22,25 +22,30 @@ https://github.com/owkin/GrAIdient/actions/workflows/examples.yml)
 Ready for the grAIt descent? <br />
 <br />
 
-GrAIdient is a framework that exposes the graph of layers as its unique way to 
-design deep learning models. This "flattened" vision enforces 
-great understanding, control and reproducibility over these models. 
+GrAIdient is a framework that allows deep learning models to be developed 
+using the internal GPU of a Mac, unlocking researchers to more easily train 
+and run AI models on their own computers.
+
+GrAIdient exposes the graph of layers, 
+providing a unique way to design deep learning models for greater understanding, 
+control and reproducibility. 
 
 Though deeply grounded to the data driven pipeline, the goal is to challenge 
 the very understanding of deep learning models and inject human intelligence 
-to go from black box models to white box models. 
+where relevant; to transition from black box models to white box models, 
+and all the gradients in between. 
 
-Let us find our grAI between them both! <br />
-<br />
+Check out this [toy VGG example](Tests/GrAIExamples/VGGExample.swift) and 
+its [documentation](Docs/Examples/VGG.md) to get started with GrAIdient today!
 
 ## ✨ Key Features
 
-- flat design with direct access to the graph of layers and the backward pass
-- run natively on Mac Intel GPU, eGPU, Mac M1, M2...
-- compiled language
-- gradient checking
+- direct access to the graph of layers and to the backward pass
+- run natively on Mac GPU (Intel GPU, eGPU, Apple Silicon)
+- written in Swift: a compiled language with strong typing
+- gradient checking to validate backward operations
 - PyTorch interoperability
-- gradients per batch & per sample (ready for differential privacy)
+- gradients per batch & per sample (needed for differential privacy)
 - debug at the neuron level
 
 ## 📦 Swift Package Manager