An Intuitive Exploration Of Artificial Intelligence
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An Intuitive Exploration of Artificial Intelligence
This book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future. An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential. The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.
Interpretable Machine Learning
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Exploring Artificial Intelligence
Exploring Artificial Intelligence: Survey Talks from the National Conference on Artificial Intelligence provides information pertinent to the distinct subareas of artificial intelligence research. This book discusses developments in machine learning techniques. Organized into six parts encompassing 16 chapters, this book begins with an overview of intelligent tutoring systems, which describes how to guide a student to learn new concepts. This text then links closely with one of the concerns of intelligent tutoring systems, namely how to interact through the utilization of natural language. Other chapters consider the various aspects of natural language understanding and survey the huge body of work that tries to characterize heuristic search programs. This book discusses as well how computer programs can create plans to satisfy goals. The final chapter deals with computational facilities that support. This book is a valuable resource for cognitive scientists, psychologists, domain experts, computer scientists, instructional designers, expert teachers, and research workers.