| 000 -LEADER |
|---|
| fixed length control field |
04213nam a22003257a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260924121526.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
260824b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9798871765302 [paperback] |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
University of Cebu-Banilad |
| Transcribing agency |
University of Cebu-Banilad |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Knowings, L.D., |
| Relator term |
author. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Building neural networks from scratch with python / |
| Statement of responsibility, etc |
L.D. Knowings. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
USA : |
| Name of publisher, distributor, etc |
Independently published, |
| Date of publication, distribution, etc |
c2023. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
173 pages ; |
| Dimensions |
22.3 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type |
volume |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Contents: Introduction - Building neural networks from scratch with python — 1. Introduction to neural networks — 2. Foundations of neural networks — 3. Implementing neural networks in python — 4. Handling complex concepts in neural networks — 5. Preparing data for neural networks — 6. Making neural networks interpretable — 7. Staying updated with neural network advancements — 8. Putting it all together: Beginner-friendly projects — Conclusion — References. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"Are you sick of these machine-learning guides that don’t really teach you anything?<br/><br/>Do you already know Python, but you’re looking to expand your horizons and skills with the language?<br/><br/>Do you want to dive into the amazing world of neural networks, but it just seems like it’s… not for you?<br/><br/>Artificial intelligence is progressing at a fantastic rate—every day, a new innovation hits the net, providing more and more opportunities for the advancement of society.<br/><br/>In your everyday life, your job, and even in your passion projects, learning how to code a neural network can be game-changing.<br/><br/>But it just seems… complicated. How do you learn everything that goes into such a complex topic without wanting to tear your own hair out?<br/><br/>Well, it just got easier.<br/><br/>Machine learning and neural networking don’t have to be complicated—with the right resources, you can successfully code your very own neural network from scratch, minimal experience needed!<br/><br/>In this all-encompassing guide to coding neural networks in Python, you’ll uncover everything you need to go from zero to hero—transforming how you code and the scope of your knowledge right before your eyes.<br/><br/>Here’s just a portion of what you will discover in this guide:<br/><br/>A comprehensive look at what a neural network is – including why you would use one and the benefits of including them in your repertoire<br/>All that pesky math dissuading you? Get right to the meat and potatoes of coding without all of those confusing equations getting you down<br/>Become a debugging master with these tips for handling code problems, maximizing your efficiency as a coder, and testing the data within your code<br/>Technological advancements galore! Learn how to keep up with all the latest trends in tech—and why doing so is important to you<br/>What in the world are layers and gradients? Detailed explanations of complex topics that will demystify neural networks, once and for all<br/>Dealing with underfitting, overfitting, and other oversights that many other resources overlook<br/>Several beginner-friendly neural network projects to put your newfound knowledge to the test<br/>And much more.<br/><br/>Imagine a world where machine learning is more accessible, where neural networks and other complex topics are available to people just like you—people with a passion. Allowing for such technological advancements is going to truly change our world.<br/><br/>It might seem hard, and you might be concerned based on other resources you’ve browsed—but this isn’t an opportunity you can pass up on! By the end of this book, you’ll have mastered neural networks confidently!" —Provided by the publisher |
| 521 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
Adult |
| 541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Source of acquisition |
Purchased |
| Deans/Chairperson |
Ortega, Eric |
| Department |
College of Computer Studies |
| Subject Category |
Computer Science |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Neural networks (Computer science). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Type of record |
Subject Reference |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) |
|---|
| Encoded by |
Gian [new] |
| Date encoded |
08/24/2026 |
