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
   Uses recurrent neural networks (RNNs), GRUs, and LSTMs to forecast time-dependent data and build a simple text translator.

• Code 7-1: Predicting Temperatures using Recurrent Neural Networks
  Demonstrates how to load the Jena Climate Dataset (a 7-year record of hourly weather data), prepare input and output sequences for time-lagged temperature prediction, and compare different RNN variants (basic RNN, stacked RNN, GRU, LSTM). Each model’s training and validation losses are plotted against a simple common-sense baseline (tomorrow’s temperature = today’s temperature). You’ll learn also to mitigate overfitting with dropout.

• Code 7-A: Translating with Recurrent Neural Network
  Implements a seq2seq translation model for English-to-Spanish sentences. It shows how to preprocess text corpora (tokenizing, normalizing, truncating/padding sequences), build vocabularies, load external word embeddings (GloVe), and define an encoder–decoder architecture. It details teacher forcing during training, testing with BLEU score, and visualizing example translations. The approach can also be adapted to other NLP tasks, including chatbot dialogues or more general text generation.

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