by Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, Carlo Manzo, Giovanni Volpe
No Starch Press, San Francisco (CA), 2025
ISBN-13: 9781718503922
https://nostarch.com/deep-learning-crash-course

1. Dense Neural Networks for Classification

2. Dense Neural Networks for Regression

3. Convolutional Neural Networks for Image Analysis

4. Encoders–Decoders for Latent Space Manipulation

5. U-Nets for Image Transformation

6. Self-Supervised Learning to Exploit Symmetries

7. Recurrent Neural Networks for Timeseries Analysis

8. Attention and Transformers for Sequence Processing

9. Generative Adversarial Networks for Image Synthesis

10. Diffusion Models for Data Representation and Exploration

11. Graph Neural Networks for Relational Data Analysis

12. Active Learning for Continuous Learning

13. Reinforcement Learning for Strategy Optimization

14. Reservoir Computing for Predicting Chaos
    Covers reservoir computing methods for forecasting chaotic systems such as the Lorenz attractor.

• Code 14-1: Training a Reservoir Computer to Predict the Lorenz System
  Trains a reservoir computer to predict the Lorenz system, a chaotic model governed by nonlinear differential equations. The reservoir, a fixed random network of neurons, transforms input data into a higher-dimensional space, while only the output weights are trained using a regularized least squares method. After generating training and validation data from the Lorenz equations, the reservoir predicts future states of the system. It successfully replicates the Lorenz attractor, capturing the system’s chaotic dynamics and providing accurate short-term forecasts despite eventual divergence due to the system’s sensitivity to initial conditions.