Deep learning : a visual approach /

"A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI"--

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Glassner, Andrew S. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: San Francisco, CA : No Starch Press, Inc., [2021]
Temas:
Deep learning (Machine learning)
Machine learning.
Neural networks (Computer science)
Apprentissage automatique.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

LEADER 00000cam a22000008i 4500
001 OR_on1201693722
003 OCoLC
005 20231017213018.0
006 m o d
007 cr |||||||||||
008 201016s2021 cau ob 001 0 eng
010 |a  2020047327 
040 |a DLC  |b eng  |e rda  |e pn  |c DLC  |d OCLCO  |d OCLCF  |d EBLCP  |d TEFOD  |d N$T  |d YDX  |d OCLCQ  |d OCLCO  |d OCLCQ  |d MNX  |d OCL 
020 |a 1718500734 
020 |a 9781718500730  |q (electronic bk.) 
020 |z 9781718500723  |q (paperback) 
029 1 |a AU@  |b 000068288702 
035 |a (OCoLC)1201693722 
037 |a 7427D980-9E48-45E5-B38B-409C741B9843  |b OverDrive, Inc.  |n http://www.overdrive.com 
042 |a pcc 
050 0 0 |a Q325.5 
082 0 0 |a 006.3/1  |2 23 
049 |a UAMI 
100 1 |a Glassner, Andrew S.,  |e author. 
245 1 0 |a Deep learning :  |b a visual approach /  |c Andrew Glassner. 
264 1 |a San Francisco, CA :  |b No Starch Press, Inc.,  |c [2021] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
505 0 |a Introduction -- Part I: Foundational ideas -- Chapter 1: An overview of machine learning -- Chapter 2: Essential statistics -- Chapter 3: Measuring performance -- Chapter 4: Bayes' Rule -- Chapter 5: Curves and surfaces -- Chapter 6: Information theory -- Part II: Basic machine learning -- Chapter 8: Training and testing -- Chapter 9: Overfitting and underfitting -- Chapter 10: Data preparation -- Chapter 11: Classifiers -- Chapter 12: Ensembles -- Part III: Deep learning basics. Chapter 13: Neural networks -- Chapter 14: Backpropagation -- Chpater 15: Optimizers -- Part IV: Beyond the basics. Chapter 16: Convolutional neural networks -- Chapter 17: Convnets in practice -- Chapter 18: Autoencoders -- Chapter 19: Recurrent neural networks -- Chapter 20: Attention and transformers -- Chapter 21: Reinforcement learning -- Chapter 22: Generatie adversarial networks -- Chapter 23: Creative applications 
520 |a "A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI"--  |c Provided by publisher 
588 0 |a Print version record and CIP data provided by publisher; resource not viewed. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Deep learning (Machine learning) 
650 0 |a Machine learning. 
650 0 |a Neural networks (Computer science) 
650 6 |a Apprentissage automatique. 
650 7 |a Neural networks (Computer science)  |2 fast  |0 (OCoLC)fst01036260 
650 7 |a Deep learning (Machine learning)  |2 fast  |0 (OCoLC)fst02032663 
650 7 |a Machine learning.  |2 fast  |0 (OCoLC)fst01004795 
776 0 8 |i Print version:  |a Glassner, Andrew S.  |t Deep learning.  |d San Francisco, CA : No Starch Press, Inc., [2021]  |z 9781718500723  |w (DLC) 2020047326 
856 4 0 |u https://learning.oreilly.com/library/view/~/9781098129019/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL6179842 
938 |a EBSCOhost  |b EBSC  |n 2383522 
938 |a YBP Library Services  |b YANK  |n 302251649 
994 |a 92  |b IZTAP 

Ejemplares similares