Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms (Record no. 6596)

MARC details
000 -LEADER
fixed length control field 01930 a2200229 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251008171220.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 251008b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9789355420121
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Item number BudF
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Buduma,Nithin
245 ## - TITLE STATEMENT
Title Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
Statement of responsibility, etc Nithin Buduma...[et al.]
250 ## - EDITION STATEMENT
Edition statement 2nd ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher SPD :
Place of publication Mumbai ,
Year of publication ©2022.
300 ## - PHYSICAL DESCRIPTION
Number of Pages xiii,372p.
520 ## - SUMMARY, ETC.
Summary, etc 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<br/>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<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Neural Networks
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Machine Learning
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Locascio, Nicholas
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Nikhil, Buduma
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Joe, Papa
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Collection code Home library Current library Shelving location Date acquired Source of acquisition Purchase Price Bill number Full call number Accession Number Print Price Bill Date/Price effective from Koha item type
      Chemical Engineering Indian Institute of Technology Tirupati Indian Institute of Technology Tirupati General Stacks 06/10/2025 Shah Book House 1260.00 SBH/28599 006.3 BudF (12156) 12156 1800.00 06/10/2025 Books