Artificial Intelligence, Machine Learning, and Deep Learning / / Oswald Campesato.

This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectu...

Full description

Saved in:
Bibliographic Details
VerfasserIn:
Place / Publishing House:Dulles, VA : : Mercury Learning and Information, , [2020]
©2020
Year of Publication:2020
Language:English
Online Access:
Physical Description:1 online resource (300 p.)
Tags: Add Tag
No Tags, Be the first to tag this record!
LEADER 03343nam a22006735i 4500
001 9781683924654
003 DE-B1597
005 20230808014301.0
006 m|||||o||d||||||||
007 cr || ||||||||
008 230808t20202020 fo d z eng d
020 |a 9781683924654 
035 |a (DE-B1597)654124 
040 |a DE-B1597  |b eng  |c DE-B1597  |e rda 
041 0 |a eng 
044 |a vau  |c US-VA 
050 4 |a TA347.A78  |b C36 2020 
072 7 |a COM079010  |2 bisacsh 
082 0 4 |a 006.3  |2 23 
100 1 |a Campesato, Oswald,   |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Artificial Intelligence, Machine Learning, and Deep Learning /  |c Oswald Campesato. 
264 1 |a Dulles, VA :   |b Mercury Learning and Information,   |c [2020] 
264 4 |c ©2020 
300 |a 1 online resource (300 p.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 0 |t Frontmatter --   |t CONTENTS --   |t Preface --   |t Chapter 1: Introduction to AI --   |t Chapter 2: Introduction to Machine Learning --   |t Chapter 3: Classifiers in Machine Learning --   |t Chapter 4: Deep Learning Introduction --   |t Chapter 5: Deep Learning: RNNs and LSTMs --   |t Chapter 6: NLP and Reinforcement Learning --   |t Appendix A: Introduction to Keras --   |t Appendix B: Introduction to TF 2 --   |t Appendix C: Introduction to Pandas --   |t Index 
506 0 |a restricted access  |u http://purl.org/coar/access_right/c_16ec  |f online access with authorization  |2 star 
520 |a This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures such as CNNs, RNNs, LSTMs, and auto encoders. Keras-based code samples are included to supplement the theoretical discussion. In addition, this book contains appendices for Keras, TensorFlow 2, and Pandas. Features: Covers an introduction to programming concepts related to AI, machine learning, and deep learningIncludes material on Keras, TensorFlow2 and Pandas 
530 |a Issued also in print. 
538 |a Mode of access: Internet via World Wide Web. 
546 |a In English. 
588 0 |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 08. Aug 2023) 
650 0 |a Artificial intelligence. 
650 0 |a Machine learning. 
650 4 |a Artificial Intelligence. 
650 4 |a Programming. 
650 7 |a COMPUTERS / Social Aspects / Human-Computer Interaction.  |2 bisacsh 
653 |a computer science. 
776 0 |c EPUB  |z 9781683924661 
776 0 |c print  |z 9781683924678 
856 4 0 |u https://www.degruyter.com/isbn/9781683924654 
856 4 2 |3 Cover  |u https://www.degruyter.com/document/cover/isbn/9781683924654/original 
912 |a EBA_BACKALL 
912 |a EBA_CL_CHCOMSGSEN 
912 |a EBA_DGALL 
912 |a EBA_EBACKALL 
912 |a EBA_EBKALL 
912 |a EBA_ECL_CHCOMSGSEN 
912 |a EBA_EEBKALL 
912 |a EBA_ESTMALL 
912 |a EBA_STMALL 
912 |a GBV-deGruyter-alles 
912 |a PDA12STME 
912 |a PDA13ENGE 
912 |a PDA18STMEE 
912 |a PDA5EBK