Khan, Arshad

Business Intelligence and Data Analysis in the Age of AI

Campesato, Oswald

Beginning Python 3 with Claude 3

A Prompt-based Exploration of LLMs

Large Language Models for Developers

Zherlitsyn, Dmytro

Financial Data Analysis Using Python

Fortino, Andres

Case Studies in Excel, R, and ChatGPT

Data Analysis for Business Decisions

Goralski, Walter

Understanding AI, IoT, 6G and The Infrastructure Revolution

Beginning Python 3 with Grok 2

Herman, Erik

Using Analytics and Case Studies

Data Science for Decision Makers

CAMPESATO, OSWALD

CSS3 and SVG with Meta AI

Erik, Herman L.

Theory, Applications and Ethics

AI Revealed

Artificial Intelligence, Machine Learning, and Deep Learning

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

AIMLDLdisc.zip

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.

This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine...

Format

Artificial Intelligence, Machine Learning, and Deep Learning

Oswald Campesato

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