Full Description
This book is the essential roadmap for anyone eager to grasp the foundational principles of artificial intelligence: no technical background required. AI Design: A Beginner's Guide demystifies core AI technologies by blending approachable language, clear analogies, and straightforward coding examples. Readers journey from the basics of teaching computers to "think" like humans, through the essential methods of machine learning: including supervised and unsupervised learning, neural networks, natural language processing, and the transformative power of models such as Transformers and LLMs. Alongside conceptual explanations, practical examples and code snippets allow readers to be hands-on, building real models for tasks like classification, clustering, and sentiment analysis: all without needing an advanced background in mathematics or programming.
Distinguished Google engineer Antonio Gulli fills a growing need for an approachable, technically accurate introduction to AI that demystifies key concepts for beginners, students, and professionals from non-technical backgrounds. Emphasizing intuition before theory and using narrative and visualization to sustain engagement, each chapter reinforces conceptual understanding with practical examples illustrating how computers "learn" patterns from data. No prior coding or mathematical background is required; minimal familiarity with computers or Python basics suffices.
The book's friendly writing style, relatable analogies, and logical progression ensure that concepts stick, while highlighting both the potential and the limitations of today's AI. Readers finish the book with the tools and confidence to not only understand AI, but to create — and critique — its applications in the real world.
Contents
1 Teaching a Computer to Think.- 2 Predicting the Future (with a Straight Line).- 3 Is this a Cat or a Dog? The Power of Classification.- 4 Making Decisions Like a Pro with Decision Trees.- 5 The Wisdom of the Crowd: Random Forests.- 6 Finding Groups in Your Data: K-Means Clustering.- 7 Your First Artificial Brain: Introduction to Neural Networks.- 8 Deep Learning and Computer Vision.- 9 Understanding Human Language: Natural Language Processing (NLP).- 10 Introducing Transformers: The "Attention" Revolution.- 11 Building the Library of Everything: LLM Pre-Training.- 12 Making the Model Yours: Fine-Tuning.- 13 Making AI Helpful and Harmless: Alignment & RLHF.- 14 AI Teaching AI: The Future with RLAIF.- 15 Your Journey as a Coder Continues.
