docs(readme): update hyperlinks

Author: marcpinet

README.md

@@ -10,7 +10,7 @@ The big part of this project, meaning the [Multilayer Perceptron (MLP)](https://
 
 I then decided to push it even further by adding [Convolutional Neural Networks (CNN)](https://en.wikipedia.org/wiki/Convolutional_neural_network),  [Recurrent Neural Networks (RNN)](https://en.wikipedia.org/wiki/Recurrent_neural_network), [Autoencoders](https://en.wikipedia.org/wiki/Autoencoder), [Variational Autoencoders (VAE)](https://en.wikipedia.org/wiki/Variational_autoencoder), [GANs](https://en.wikipedia.org/wiki/Generative_adversarial_network) and [Transformers](https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)).
 
-Regarding the Transformers, I just basically reimplement the [Attention is All You Need](https://arxiv.org/abs/1706.03762) paper, but I had some issues with the gradients and the normalization of the attention weights. So, I decided to leave it as it is for now. It theorically works but needs a huge amount of data that can't be trained on a CPU. You can however see what each layers produce and how the attention weights are calculated [here](examples/generation/transformer-text-generation/transformer-debug.ipynb).
+Regarding the Transformers, I just basically reimplement the [Attention is All You Need](https://arxiv.org/abs/1706.03762) paper, but I had some issues with the gradients and the normalization of the attention weights. So, I decided to leave it as it is for now. It theorically works but needs a huge amount of data that can't be trained on a CPU. You can however see what each layers produce and how the attention weights are calculated [here](examples/models-usages/generation/transformer-text-generation/transformer-debug.ipynb).
 
 This project will be maintained as long as I have ideas to improve it, and as long as I have time to work on it.
 
@@ -40,20 +40,20 @@ pip install neuralnetlib
 
 ## Basic usage
 
-See [this file](examples/classification-regression/mnist_multiclass.ipynb) for a simple example of how to use the library.<br>
-For a more advanced example, see [this file](examples/cnn-classification/cnn_classification_mnist.ipynb) for using CNN.<br>
-You can also check [this file](examples/classification-regression/sentiment_analysis.ipynb) for text classification using RNN.<br>
+See [this file](examples/models-usages/mlp-classification-regression/mnist_multiclass.ipynb) for a simple example of how to use the library.<br>
+For a more advanced example, see [this file](examples/models-usages/cnn-classification/cnn_classification_mnist.ipynb) for using CNN.<br>
+You can also check [this file](examples/models-usages/mlp-classification-regression/sentiment_analysis.ipynb) for text classification using RNN.<br>
 
 ## Advanced usage
 
-See [this file](examples/generation/autoencoder_vae_example.ipynb) for an example of how to use VAE to generate new images.<br>
-And [this file](examples/rnn-text-generation/dinosaur_names_generator.ipynb) for an example of how to generate new dinosaur names.<br>
+See [this file](examples/models-usages/generation/autoencoder_vae_example.ipynb) for an example of how to use VAE to generate new images.<br>
+And [this file](examples/models-usages/rnn-text-generation/dinosaur_names_generator.ipynb) for an example of how to generate new dinosaur names.<br>
 
 More examples in [this folder](examples).
 
 You are free to tweak the hyperparameters and the network architecture to see how it affects the results.
 
-## 🚀 Quick examples (more [here](examples/))
+## 🚀 Quick examples (more [here](examples/models-usages/))
 
 ### Binary Classification