Artificial Intelligence, Machine Learning, and Deep Learning

✍ Scribed by Oswald Campesato

• Publisher: Mercury Learning and Information
• Year: 2020
• Tongue: English
• Leaves: 339
• Category: Library

✦ Synopsis

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 learning
• Includes material on Keras, TensorFlow2 and Pandas

✦ Table of Contents

Preface: The ML and DL Landscape
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