Deep Learning Crash Course

by Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, and Carlo Manzo

Fall 2025, 472 pp.

ISBN-13:

9781718503922

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.

Author Bio

Giovanni Volpe is a professor in the Physics Department of the University of Gothenburg, Sweden, and head of the Soft Matter Lab. His research interests include deep learning, brain connectivity, statistical mechanics, and soft matter. He has authored more than 200 articles and reviews on these topics and has co-authored two books. He has also developed several software packages for microscopy, deep learning, and brain connectivity.

Joana B. Pereira is an associate professor at Karolinska Institute in Sweden, where she focuses on investigating new biomarkers for neurodegenerative disorders, in particular Alzheimer’s disease. Since 2020, she has organized an annual interdisciplinary conference called Emerging Topics in Artificial Intelligence in San Diego, CA. She is the scientific coordinator for NeurotechEU and winner of the 2021 de Leon Prize for best neuroimaging article in Alzheimer’s disease.

Carlo Manzo is an associate professor at the University of Vic, Spain, where he leads the Quantitative Bioimaging Lab. His research is dedicated to the analysis of biophysical processes, merging deep learning techniques with state-of-the-art imaging technologies to achieve single-molecule sensitivity. He is the founder of the Anomalous Diffusion challenge and winner of the 2017 E. Pérez Payá Prize from the Sociedad de Biofísica de España.

Benjamin Midtvedt is a doctoral researcher working to enhance the field of microscopy through deep learning. The unifying focus of his research has been the development of accessible and practical AI optimized to the needs of the user. He has also been the lead developer of several Python-based open-source deep learning frameworks.

Jesús Pineda is a doctoral researcher in physics focused on the intersection between
deep learning and computer vision. He has co-authored several articles in high-impact journals on the application of deep learning to glean insights from microscopy data. Jesús is also a core developer of the deep learning software packages DeepTrack and Deeplay.

Henrik Klein Moberg is a Ph.D. candidate at Chalmers University of Technology specializing in the integration of AI with physical sciences. His research focuses on applying deep learning techniques to nanofluidic microscopy and nanophotonics, aiming to enhance the precision and efficiency of these technologies. He has also organized and spoken at numerous conferences related to AI and scientific data analysis.

Harshith Bachimanchi is a PhD student whose research combines holographic microscopy and deep learning to better understand marine microorganisms. Harshith has used deep learning techniques and experimental optics to track both biological and synthetic particles, enhancing our understanding of these complex systems. He has also developed simulations demonstrating the applications of deep learning in microscopy.

Table of contents

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

The chapters in red are included in this Early Access PDF.