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Deep Learning Summary/Overview

Useful document to review or obtain an quick overview of DL algorithms from simple MLP to Transformers. 

It also contains short snippets to illustrate main concepts

Neural Networks → CNNs → RNNs → LSTMs → Transformers & Beyond 

Contents 
1.  What is a Neural Network? Neurons, Layers, Activation Functions 
2.  Forward Propagation — How predictions are made 
3.  Backpropagation & Optimisers — How models learn 
4.  Building a Multilayer Perceptron (MLP) with Keras & PyTorch 
5.  Regularisation — Dropout, Batch Norm, L1/L2, Early Stopping 
6.  Convolutional Neural Networks (CNN) 
7.  Sequence Models — RNN & LSTM 
8.  Transformers & Attention Mechanism 
9.  Other Key Deep Learning Topics — Transfer Learning, GANs, Autoencoders 
10.  Master Cheat Sheet & Interview Prep