Deep Learning Crash Course

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.

Table of Contents:
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

About the Author :
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.