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...
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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.) |
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Other title: | Frontmatter -- CONTENTS -- Preface -- Chapter 1: Introduction to AI -- Chapter 2: Introduction to Machine Learning -- Chapter 3: Classifiers in Machine Learning -- Chapter 4: Deep Learning Introduction -- Chapter 5: Deep Learning: RNNs and LSTMs -- Chapter 6: NLP and Reinforcement Learning -- Appendix A: Introduction to Keras -- Appendix B: Introduction to TF 2 -- Appendix C: Introduction to Pandas -- Index |
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Summary: | 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 |
Format: | Mode of access: Internet via World Wide Web. |
ISBN: | 9781683924654 |
Access: | restricted access |
Hierarchical level: | Monograph |
Statement of Responsibility: | Oswald Campesato. |