Deep Learning book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, published by The MIT Press. The cover features an artistic depiction of a landscape with trees, flowers, and abstract elements, blending natural and surreal imagery. Ideal for readers interested in machine learning, AI, and deep learning techniques.
The MIT Press
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Deep Learning

Key item features

  • The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. 
  • It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. 
  • Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

  • An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
  • “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
  • —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
  • Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

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