This comprehensive, hands-on guide to deep learning with Python covers fundamental concepts and advanced techniques to apply deep neural network models in real-world scenarios. <Deep Learning Crash Course< starts from the basics to explore the most;modern techniques and applications that are of great interest right now, and whose popularity will only grow in the future. It covers advanced topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the technology behind ChatGPT), diffusion models (the technology behind text2image models such as DALL-E), graph neural networks (the technology behind AlphaFold), and deep reinforcement learning (the technology behind AlphaGo). These cutting-edge concepts and techniques address the current demands and trends in deep learning, giving you practical skills to tackle complex real-world problems.

Autorentext
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.

Klappentext

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.

Zusammenfassung
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.

  Inhalt
  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