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Fundamentals of deep learning : designing next-generation machine intelligence algorithms / Nithin Buduma, Nikhil Buduma, and Joe Papa ; with contributions by Nicholas Locascio.

By: Contributor(s): Material type: TextPublication details: New Delhi: Shroff Publishers and Distributors Pvt.Ltd., 2022Edition: Second editionDescription: xiii, 372 pages : illustrations ; 24 cmISBN:
  • 9781492082187
  • 9789355420121
Subject(s): DDC classification:
  • 006.31 23 B9279f
Contents:
Fundamentals of linear algebra for deep learning -- Fundamentals of probability -- The neural network -- Training feed-forward neural networks -- Implementing neural networks in PyTorch -- Beyond gradient descent -- Convolutional neural networks -- Embedding and representation learning -- Models for sequence analysis -- Generative models -- Methods in interpretability -- Memory augmented neural networks -- Deep reinforcement learning.
Summary: We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics. The updated second edition of this book describes the intuition behind these innovations without jargon or complexity.
List(s) this item appears in: New Arrivals April to July 2026
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Holdings
Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Books Library, Independent University, Bangladesh (IUB) General Stacks 006.31 B9279f (Browse shelf(Opens below)) 2022 Available 029690
Books Library, Independent University, Bangladesh (IUB) Reference Stacks 006.31 B9279f (Browse shelf(Opens below)) 2022 02 Not For Loan 029691
Total holds: 0

Previous edition: published as by Nikhil Buduma with contributions by Nicholas Locascio. 2017.

Includes bibliographical references and index.

Fundamentals of linear algebra for deep learning -- Fundamentals of probability -- The neural network -- Training feed-forward neural networks -- Implementing neural networks in PyTorch -- Beyond gradient descent -- Convolutional neural networks -- Embedding and representation learning -- Models for sequence analysis -- Generative models -- Methods in interpretability -- Memory augmented neural networks -- Deep reinforcement learning.

We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics. The updated second edition of this book describes the intuition behind these innovations without jargon or complexity.

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