Deep Learning

# Deep Learning

Neural networks from the single perceptron to modern transformers: how depth, the right activations, and gradient-based training let a model learn its own features instead of hand-crafted ones.

**Prerequisites:** the [Machine Learning](/en/Machine%20Learning) course, especially [Multilayer neural networks](/en/Machine%20Learning/06%20Multilayer%20neural%20networks), which builds the model and covers the losses and backpropagation used throughout this course. Basic Python, calculus, and linear algebra.

## Syllabus

1. [Introduction](/en/Deep%20Learning/01%20Introduction)
2. [Activation functions](/en/Deep%20Learning/02%20Activation%20functions)
3. [Optimization](/en/Deep%20Learning/03%20Optimization)
4. [Training deep networks](/en/Deep%20Learning/04%20Training%20deep%20networks)
5. [Convolutional networks](/en/Deep%20Learning/05%20Convolutional%20networks)
6. [Embeddings and representation learning](/en/Deep%20Learning/06%20Embeddings%20and%20representation%20learning)
7. [Recurrent networks](/en/Deep%20Learning/07%20Recurrent%20networks)
8. [LSTM and GRU](/en/Deep%20Learning/08%20LSTM%20and%20GRU)
9. [Attention](/en/Deep%20Learning/09%20Attention)
10. [Transformers](/en/Deep%20Learning/10%20Transformers)

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