GenAI on AWS

Olivier Bergeret , Asif Abbasi , Joel Farvault 지음

Wiley 2025년 03월 19일 출간

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The definitive guide to leveraging AWS for generative AI GenAI on AWS: A Practical Approach to Building Generative AI Applications on AWS is an essential guide for anyone looking to dive into the world of generative AI with the power of Amazon Web Services (AWS). Crafted by a team of experienced cloud and software engineers, this book offers a direct path to developing innovative AI applications. It lays down a hands-on roadmap filled with actionable strategies, enabling you to write secure, efficient, and reliable generative AI applications utilizing the latest AI capabilities on AWS. This comprehensive guide starts with the basics, making it accessible to both novices and seasoned professionals. You'll explore the history of artificial intelligence, understand the fundamentals of machine learning, and get acquainted with deep learning concepts. It also demonstrates how to harness AWS's extensive suite of generative AI tools effectively. Through practical examples and detailed explanations, the book empowers you to bring your generative AI projects to life on the AWS platform. In the book, you'll: Gain invaluable insights from practicing cloud and software engineers on developing cutting-edge generative AI applications using AWS Discover beginner-friendly introductions to AI and machine learning, coupled with advanced techniques for leveraging AWS's AI tools Learn from a resource that's ideal for a broad audience, from technical professionals like cloud engineers and software developers to non-technical business leaders looking to innovate with AI Whether you're a cloud engineer, software developer, business leader, or simply an AI enthusiast, Gen AI on AWS is your gateway to mastering generative AI development on AWS. Seize this opportunity for an enduring competitive advantage in the rapidly evolving field of AI. Embark on your journey to building practical, impactful AI app

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Acknowledgments xv About the Authors xvii Foreword xix Introduction xxi Chapter 1: A Brief History of AI 1 The Precursors of the Mechanical or “Formal” Reasoning 2 The Digital Computer Era 4 Cybernetics and the Beginning of the Robotic Era 6 Birth of AI and Symbolic AI (1955 1985) 10 Subsymbolic AI Era (1985 2010) 14 Deep Learning and LLM (2010 Present) 16 Key Takeaways 17 Chapter 2: Machine Learning 19 What Is Machine Learning? 19 Types of Machine Learning 20 Supervised Learning 21 Unsupervised and Semi-Supervised Learning 22 Reinforcement Learning 23 Methodology for Machine Learning 24 Implementation of Machine Learning 26 Machine Learning Applications 27 Natural Language Processing (NLP) 27 Computer Vision 27 Recommender System 27 Predictive Analytics 28 Fraud Detection 28 Machine Learning Frameworks and Libraries 28 TensorFlow 28 PyTorch 31 Scikit-learn 34 Keras 35 Apache Spark MLlib 37 Future Trends in Machine Learning 40 Rise of Edge Computing and Edge AI 40 Convergence with Emerging Technologies 40 Advancements in Unsupervised Learning, Reinforcement Learning, and Generative Models 41 Increased Specialization and Customization 41 Explainable and Trustworthy AI 42 Key Takeaways 42 References 43 Chapter 3: Deep Learning 45 Deep Learning vs. Machine Learning 45 Computer Vision Example 46 Natural Language Processing Example 47 The History of Deep Learning 47 Understanding Deep Learning 52 Neurons 52 Weights and Biases 54 Layers 54 Activation Function(s) 55 An Introduction to the Perceptron 58 Overcoming Perceptron Limitations 59 FeedForward Neural Networks 60 Backpropagation 60 Para

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저자(글) Olivier Bergeret

저자(글) Asif Abbasi

저자(글) Joel Farvault