Deep Learning Crash Course

Giovanni Volpe, Benjamin Midtvedt, Jesus Pineda , Henrik Klein Moberg

$130

Paperback

Forthcoming
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English

No Starch Press,US
06 January 2026

Computer programming & software development; Machine learning

A complete guide to deep neural networks - the technology behind AI - covering fundamental and advanced techniques to apply machine learning in real-world scenarios.

Build AI Models from Scratch (No PhD Required)

Deep Learning Crash Course is a fast-paced, thorough introduction that will have you building today's most powerful AI models from scratch. No experience with deep learning required!

Designed for programmers who may be new to deep learning, this book offers practical, hands-on experience, not just an abstract understanding of theory.

You'll start from the basics, and using PyTorch with real datasets, you'll quickly progress from your first neural network to advanced architectures like convolutional neural networks (CNNs), transformers, diffusion models, and graph neural networks (GNNs). Each project can be run on your own hardware or in the cloud, with annotated code available on GitHub.

You'll build and train models to-

Classify and analyze images, sequences, and time series Generate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion models Process natural language with recurrent neural networks and transformers Model molecules and physical systems with graph neural networks Improve continuously through reinforcement and active learning Predict chaotic systems with reservoir computing

Whether you're an engineer, scientist, or professional developer, you'll gain fluency in deep learning and the confidence to apply it to ambitious, real-world problems. With Deep Learning Crash Course, you'll move from using AI tools to creating them.

By: Giovanni Volpe, Benjamin Midtvedt, Jesus Pineda, Henrik Klein Moberg, Harshith Bachimanchi
Imprint: No Starch Press,US
Country of Publication: United States
Dimensions: Height: 234mm, Width: 177mm,
Weight: 369g
ISBN: 9781718503922
ISBN 10: 171850392X
Pages: 672
Publication Date: 06 January 2026
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Forthcoming

Introduction Chapter 1: Dense Neural Networks for Classification Chapter 2: Dense Neural Networks for Regression Chapter 3: Convolutional Neural Networks for Image Analysis Chapter 4: Encoders–Decoders for Latent Space Manipulation Chapter 5: U-Nets for Image Transformation Chapter 6: Self-Supervised Learning to Exploit Symmetries Chapter 7: Recurrent Neural Networks for Timeseries Analysis Chapter 8: Attention and Transformers for Sequence Processing Chapter 9: Generative Adversarial Networks for Image Synthesis Chapter 10: Diffusion Models for Data Representation and Exploration Chapter 11: Graph Neural Networks for Relational Data Analysis Chapter 12: Active Learning for Continuous Learning Chapter 13: Reinforcement Learning for Strategy Optimization Chapter 14: Reservoir Computing for Predicting Chaos Conclusion and Outlook

Giovanni Volpe, head of the Soft Matter Lab at the University of Gothenburg and recipient of the G ran Gustafsson Prize in Physics, has published extensively on deep learning and physics and developed key software packages including DeepTrack, Deeplay, and BRAPH. Benjamin Midtvedt and Jesos Pineda are core developers of DeepTrack and Deeplay. Henrik Klein Moberg and Harshith Bachimanchi apply AI to nanoscience and holographic microscopy. Joana B. Pereira, head of the Brain Connectomics Lab at the Karolinska Institute, organizes the annual conference Emerging Topics in Artificial Intelligence. Carlo Manzo, head of the Quantitative Bioimaging Lab at the University of Vic, is the founder of the Anomalous Diffusion Challenge.