by Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, Carlo Manzo
No Starch Press, San Francisco (CA), 2026
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
    Explains Q-learning and Deep Q-learning by teaching an agent to master games such as Tetris.

• Code 13-1: Teaching a Deep Q-Learning Agent to Play Tetris
  Demonstrates how to implement a deep reinforcement learning agent to play Tetris by using Deep Q-Learning to overcome the challenges posed by the game's large state space and complex dynamics. The example begins with adapting the Tetris environment to enable efficient interaction for reinforcement learning. The agent employs a neural network to approximate Q-values, allowing it to make decisions without relying on a traditional Q-table. To balance exploration and exploitation, an epsilon-greedy strategy is used, where randomness gradually decreases as the agent learns. A replay buffer is implemented to store past experiences, ensuring diverse training data that enhances stability during learning. The agent refines its policy through iterative training, guided by the Bellman equation, leading to consistent improvements. As training progresses, the agent effectively handles randomized tile sequences and achieves performance levels comparable to skilled human players.

14. Reservoir Computing for Predicting Chaos