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
   Focuses on autoencoders, variational autoencoders, Wasserstein autoencoders, and anomaly detection, enabling data compression and generation.

• Code 4-1: Denoising Images with a Denoising Encoder-Decoder
  Implements a denoising encoder–decoder that cleans noisy microscopy images. It illustrates how to simulate noisy vs. clean pairs, train a small convolutional model to learn noise removal, and verify that the network isn’t simply memorizing one output (checking for mode collapse).

• Code 4-A: Generating Digit Images with Variational Autoencoders
  Shows how to implement a variational autoencoder (VAE) on MNIST digits. The encoder outputs a mean and variance for latent variables, and the decoder reconstructs digits from sampled latent points. The notebook demonstrates random sampling to create new digit images and visualizes how the VAE organizes digits in the latent space.

• Code 4-B: Morphing Images with Wasserstein Autoencoders
  Explores a Wasserstein autoencoder (WAE) trained on Fashion-MNIST. You’ll see how to decode random latent points to generate clothing/accessory images and how to interpolate (morph) one image into another in latent space, showcasing the smooth transitions that WAEs learn.

• Code 4-C: Detecting ECG Anomalies with an Autoencoder
  Uses a 1D convolutional autoencoder to detect abnormal heartbeats in ECG data. It’s trained exclusively on normal ECGs so that reconstruction error (or distance in latent space) highlights anomalies that deviate from learned “normal” patterns. Demonstrates how to set a threshold for detecting anomalous signals and compares two approaches: reconstruction-based vs. neighbor-based in latent space.

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