AI Fundamentals

By Dr. Elena Martinez · 2024 Edition

Category: Artificial Intelligence
Pages: 320
ISBN: 978-3-16-148410-0

Overview

"AI Fundamentals" is a comprehensive guide to core concepts in artificial intelligence and machine learning. This open-access textbook provides both theoretical foundations and practical implementations using modern frameworks likeTensorFlow and PyTorch. Ideal for students, researchers, and professionals aiming to deepen their understanding of intelligent systems.

30+

Chapters

660+

Examples

Case Studies

Table of Contents Beta Edition

Introduction to AI
Foundations of Machine Learning New
Deep Learning Fundamentals
Neural Network Architectures
Reinforcement Learning

Key Features

Open Access

Comprehensive Coverage

Explores both theoretical foundations and modern practical implementations of AI technologies

Interactive

Hands-on Examples

Includes over 660 code examples, datasets, and sandbox exercises available directly from your browser

Sample Content

Chapter 2: Machine Learning Foundations

This chapter introduces fundamental concepts of machine learning including: supervised and unsupervised learning, regression, clustering and evaluation metrics...

Author Bio

Dr. Elena Martinez

Lead AI Reseacher · Stanford University

Dr. Elena Martinez is an associate professor of artificial intelligence at Stanford University and founder of the Cognitive Algorithms Lab. With over 25 peer-reviewed publications and 30+ conferences, she specializes in neural network optimization and AI ethics.

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Citations & References

Goodfellow I, Bengio Y, Courville A. Deep Learning 2016. MIT Press

ISBN: 9780262035613

Russel S, Norvig P. Artificial Intelligence 3rd Edition. Prentice Hall 2010.

ISBN: 9780132071482

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