| 000 -LEADER |
|---|
| fixed length control field |
nam a22 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20230913115757.0 |
| 020 ## - ISBN |
|---|
| International standerd number |
9789355420121 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
AEF |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Edition number |
23rd ed. |
| Classification number |
006.31 |
| Item number |
BUD |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Boduma, N. |
| 9 (RLIN) |
3258 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Fundamentals of deep learning |
| Remainder of title |
designing next generation machine intelligence algorithams |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Mumbai: |
| Name of publisher, distributor, etc. |
Shroff publishers and distributers, |
| Date of publication, distribution, etc. |
2022 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Biging: |
| Name of publisher, distributor, etc. |
O'Reilly, |
| Date of publication, distribution, etc. |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
373 p.: |
| Dimensions |
23 cm |
| 521 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
<br/><br/>All Indian Reprints of O'Reilly are printed in Grayscale<br/><br/>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.<br/><br/>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.<br/><br/> Learn the mathematics behind machine learning jargon<br/> Examine the foundations of machine learning and neural networks<br/> Manage problems that arise as you begin to make networks deeper<br/> Build neural networks that analyze complex images<br/> Perform effective dimensionality reduction using autoencoders<br/> Dive deep into sequence analysis to examine language<br/> Explore methods in interpreting complex machine learning models<br/> Gain theoretical and practical knowledge on generative modeling<br/> Understand the fundamentals of reinforcement learning<br/><br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning |
| 9 (RLIN) |
3167 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Deep learning (Machine learning) |
| 9 (RLIN) |
3259 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Buduma, Nikhil |
| 9 (RLIN) |
3260 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Papa, Joe |
| 9 (RLIN) |
3261 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Book |
