Chapter 3. Deep learning and neural networks

6.1. An intuitive approach to deep learning

6.2. Neural networks

6.3. The perceptron

6.3.1. Training

6.3.2. Training a perceptron in scikit-learn

6.3.3. A geometric interpretation of the perceptron for two inputs

6.4. Multilayer perceptrons

6.4.1. Training using backpropagation

6.4.2. Activation functions

6.4.3. Intuition behind backpropagation

6.4.4. Backpropagation theory

6.4.5. MLNN in scikit-learn

6.4.6. A learned MLP

6.5. Going deeper: from multilayer neural networks to deep learning

6.5.1. Restricted Boltzmann Machines

6.5.2. The Bernoulli Restricted Boltzmann Machine

6.5.3. RBMs in action

6.6. Summary

What’s inside