Fundamentals of deep learning: designing next generation machine intelligence algorithms

By:

Buduma, Nithin

Contributor(s):

Publication details: Beijing: O'Reilly Media, 2022.Edition: 2ndDescription: xiii, 372p.: ill; pbk: 23cmISBN:

9789355420121

Subject(s):

DDC classification:

006.31 BUD

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. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field. -Learn the mathematics behind machine learning jargon -Examine the foundations of machine learning and neural networks -Manage problems that arise as you begin to make networks deeper -Build neural networks that analyze complex images -Perform effective dimensionality reduction using autoencoders -Dive deep into sequence analysis to examine language -Explore methods in interpreting complex machine learning models -Gain theoretical and practical knowledge on generative modeling -Understand the fundamentals of reinforcement learning https://www.oreilly.com/library/view/fundamentals-of-deep/9781492082170/

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar	General	006.31 BUD (Browse shelf(Opens below))	1	Checked out	02/08/2024	033185

Includes bibliographical references and index.

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. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field.
-Learn the mathematics behind machine learning jargon
-Examine the foundations of machine learning and neural networks
-Manage problems that arise as you begin to make networks deeper
-Build neural networks that analyze complex images
-Perform effective dimensionality reduction using autoencoders
-Dive deep into sequence analysis to examine language
-Explore methods in interpreting complex machine learning models
-Gain theoretical and practical knowledge on generative modeling
-Understand the fundamentals of reinforcement learning

https://www.oreilly.com/library/view/fundamentals-of-deep/9781492082170/

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