The AI Model Handbook: A guide to the world of artificial intelligence modeling
by Minh Trinh
- Length: 249 pages
- Edition: 1
- Language: English
- Publisher: Rodeo Press
- Publication Date: 2021-12-26
- ISBN-10: B09JN6BXD9
- ISBN-13: 9798985117240
- Sales Rank: #0 (See Top 100 Books)
This book introduces in a non-technical way artificial intelligence, machine learning, and the most common models used in production. It covers supervised and unsupervised learning, deep learning, natural language processing, computer vision, generative adversarial networks, graph neural networks, recommender systems, and causal inference.
PrefaceAI in the New PC Who Should Read This Book? Outline of This Book Online Resources Acknowledgments Chapter 1Artificial Intelligence and Machine Learning 1.1 What is Artificial Intelligence? 1.2 What is Machine Learning? 1.3 The Categories of Machine Learning 1.4 Yann LeCun’s Cake 1.5 Where is the Cake? Chapter 2Supervised Machine Learning Models 2.1 Supervised Machine Learning 2.2 Training, Validation, and Test Sets 2.3 Regression 2.4 Classification Chapter 3Unsupervised Machine Learning Models 3.1 Unsupervised Machine Learning 3.2 Dimensionality Reduction 3.3 Clustering 3.4 Models Chapter 4Deep Learning Models 4.1 Deep Learning 4.2 Feedforward Neural Network 4.3 Model Training 4.4 Model Regularization 4.5 Model Prediction 4.6 Model Monitoring Chapter 5Reinforcement Learning Models 5.1 What is Reinforcement Learning 5.2 Dynamic Programming 5.3 Model-free Reinforcement Learning 5.4 RL with Function Approximation Chapter 6Natural Language Processing (NLP) Models 6.1 The Field of Natural Language Processing 6.2 Language Tasks 6.3 Classical NLP Modelling 6.4 Deep NLP Modelling 6.5 Conclusion Chapter 7Computer Vision Models 7.1 Vision Recognition 7.2 Convolutional Networks 7.3 Benchmarks 7.4 Convolutional Network Models Chapter 8Generative Adversarial Networks 8.1 What are Generative Adversarial Networks 8.2 How do GANs work 8.3 Different GANs Chapter 9Graph Neural Networks 9.1 Graphs 9.2 Machine Learning for Graphs 9.3 Graph Neural Networks Chapter 10Recommender Systems 10.1 What Is A Recommender System? 10.2 Different Approaches to Recommender Systems 10.3 Metrics Chapter 11Causal Inference Models 11.1 Causal Inference 11.2 Causal Diagram 11.3 Causality Models 11.4 Causal Machine Learning 11.5 Applications Conclusion The State of AI Modeling Where Do We Go From Here: Le Buffet The Journey Continues Appendix A: Pioneers in Artificial Intelligence Appendix B: The AI Agile Manifesto References
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