Deep Learning Crash Course

Giovanni Volpe

ISBN 10: 171850392X / ISBN 13: 9781718503922

Published by Random House LLC US Jan 2026, 2026

About this Item

Neuware -A complete guide to deep neural networks the technology behind AI covering fundamental and advanced techniques to apply machine learning in real-world scenarios.Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future.The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo).This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 680 pp. Englisch.

Seller Inventory # 9781718503922

Bibliographic Details

• Title: Deep Learning Crash Course
• Publisher: Random House LLC US Jan 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 171850392X
• ISBN 13: 9781718503922
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 181H x 35D millimeters 235 millimeters width by 181 millimeters height by 35 millimeters depth
• Weight: 1062 grams

About this title

Synopsis

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.

About the Author

Giovanni Volpe, head of the Soft Matter Lab at the University of Gothenburg and recipient of the Göran 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 Jesús 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.