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Deep Learning Crash Course
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
Classify and analyze images, sequences, and time seriesGenerate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion modelsProcess natural language with recurrent neural networks and transformersModel molecules and physical systems with graph neural networksImprove continuously through reinforcement and active learningPredict 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.
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
Classify and analyze images, sequences, and time seriesGenerate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion modelsProcess natural language with recurrent neural networks and transformersModel molecules and physical systems with graph neural networksImprove continuously through reinforcement and active learningPredict 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.
672 pages, Kindle Edition
Published January 6, 2026
3 people are currently reading
7 people want to read
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