Giovanni Volpe: Deep Learning Crash Course

Cover: 9781718503922 | Deep Learning Crash Course | Giovanni Volpe (u. a.) | Taschenbuch

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Sprache: Englisch

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Beschreibung

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.

Über den Autor

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.

Inhaltsverzeichnis

Introduction
Chapter 1: Building and Training Your First Neural Network
Chapter 2: Capturing Trends and Recognizing Patterns with Dense Neural Networks
Chapter 3: Processing Images with Convolutional Neural Networks
Chapter 4: Enhancing, Generating, and Analyzing Data with Autoencoders
Chapter 5: Segmenting and Analyzing Images with U-Nets
Chapter 6: Training Neural Networks with Self-Supervised Learning
Chapter 7: Processing Time Series and Language with Recurrent Neural Networks
Chapter 8: Processing Language and Classifying Images with Attention and Transformers
Chapter 9: Creating and Transforming Images with Generative Adversarial Networks
Chapter 10: Implementing Generative AI with Diffusion Models
Chapter 11: Modeling Molecules and Complex Systems with Graph Neural Networks
Chapter 12: Continuously Improving Performance with Active Learning
Chapter 13: Mastering Decision-Making with Deep Reinforcement Learning
Chapter 14: Predicting Chaos with Reservoir Computing
Conclusion
Index

Erscheinungsjahr:	2026
Genre:	Importe , Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	Einband - flex.(Paperback)
ISBN-13:	9781718503922
ISBN-10:	171850392X
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Volpe, Giovanni Midtvedt, Benjamin Pineda, Jesús Klein Moberg, Henrik Bachimanchi, Harshith Pereira, Joana B. Manzo, Carlo
Hersteller:	Random House LLC US No Starch Press
Verantwortliche Person für die EU:	Springer Fachmedien Wiesbaden GmbH, Postfach:15 46, D-65189 Wiesbaden, info@bod.de
Maße:	235 x 181 x 35 mm
Von/Mit:	Giovanni Volpe (u. a.)
Erscheinungsdatum:	06.01.2026
Gewicht:	1,062 kg