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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.
Producent/osoba odpowiedzialna za bezpieczeństwo produktu
Phaidon SARL
gpsr@phaidon.com
+33 1 55 28 38 39
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.
Producent/osoba odpowiedzialna za bezpieczeństwo produktu
Phaidon SARL
gpsr@phaidon.com
+33 1 55 28 38 39
Szczegóły
Szczegóły
Data wydania: 06.01.2026
Liczba stron: 680
Wymiary: 17.7x23.4
Typ okładki:miękka okładka
Wydawca: No Starch Press,US
Tytuł:Deep Learning Crash Course
EAN: 9781718503922
Recenzje
Recenzje
Produkt nie ma jeszcze recenzji.
Zamieszczenie recenzji nie wymaga logowania. Sklep nie prowadzi weryfikacji, czy autorzy recenzji nabyli lub użytkowali dany produkt.
Nasza cena:221,71 zł
Cena katalogowa dostawcy: 295,90 zł
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