An Introduction to Deep Learning is the complete guide to writing deep learning programs with the widely-used Python language and TensorFlow programming environment.   Building on his pioneering undergraduate and graduate courses, Brown University professor Eugene Charniak covers every key concept and technique, including feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and more. Each chapter contains a full programming project and a set of exercises carefully crafted to help readers build mastery, as well as additional readings and references for even deeper insight.

An Introduction to Deep Learning (Addison-Wesley Data & Analytics Series)

Computers & Technology

Charniak, Eugene

Computer Science

Addison-Wesley Professional

Slight shelf wear to DJ and boards. Pages are clean. Binding is tight.

Introduction to Artificial Intelligence (Addison-Wesley Series in Computer Science)

McDermott, Drew

Reference

Addison-Wesley

Eugene Charniak breaks new ground in artificial intelligenceresearch by presenting statistical language processing from an artificial intelligence point of view in a text for researchers and scientists with a traditional computer science background.New, exacting empirical methods are needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine translation, and natural language processing (NLP). It is time, Charniak observes, to switch paradigms. This text introduces statistical language processing techniques ;word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic wordclasses, word-sense disambiguation ;along with the underlying mathematics and chapter exercises.Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages. It is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning: "one simply gathers statistics."Language, Speech, and Communication

Statistical Language Learning (Language, Speech, and Communication)

Bradford Books

A project-based guide to the basics of deep learning.
This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.
Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Introduction to Deep Learning (Mit Press)

The MIT Press

Eugene Charniak breaks new ground in artificial intelligence research by presenting statistical language processing from an artificial intelligence point of view in a text for researchers and scientists with a traditional computer science background. New, exacting empirical methods are needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine translation, and natural language processing (NLP). It is time, Charniak observes, to switch paradigms. This text introduces statistical language processing techniques; word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic wordclasses, word-sense disambiguation; along with the underlying mathematics and chapter exercises. Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages. It is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning: "one simply gathers statistics."

Statistical Language Learning (Language, Speech, and Communication) (Language, Speech and Communication Series)

MIT Press]

A concise and illuminating history of the field of artificial&nbsp;intelligence from one of its earliest and most respected&nbsp;pioneers. AI & I is an intellectual history of the field of artificial&nbsp;intelligence from the perspective of one of its first&nbsp;practitioners, Eugene Charniak. Charniak entered the field in 1967, roughly 12 years&nbsp;after AI's founding, and was involved in many of AI's formative&nbsp;milestones. In this book, he traces the trajectory of breakthroughs&nbsp;and disappointments of the discipline up to the current day, clearly and engagingly demystifying this oft revered and misunderstood technology. His argument is controversial but well supported: that classical AI has been almost uniformly unsuccessful&nbsp;and that the modern deep learning approach should be&nbsp;viewed as the foundation for all the exciting developments that&nbsp;are to come. Written for the scientifically educated layperson, this book chronicles the history of the field of AI, starting with its origin in 1956, as a topic for a small academic workshop held at Dartmouth University. From there, the author covers reasoning and knowledge representation, reasoning under uncertainty, chess, computer vision, speech recognition, language acquisition, deep learning, and learning writ large. Ultimately, Charniak takes issue with the controversy of AI--the fear that its invention means the end of jobs, creativity, and potentially even humans as a species--and explains why such concerns are unfounded. Instead, he believes that we should embrace the technology and all its potential to benefit society.

AI & I: An Intellectual History of Artificial Intelligence

Artificial intelligence research has thrived in the years since this best-selling AI classic was first published. The revision encompasses these advances by adapting its coding to Common Lisp, the well-documented language standard, and by bringing together even more useful programming tools. Today's programmers in AI will find this volume's superior coverage of programming techniques and easily applicable style anything but common.

Artificial Intelligence Programming

McDermott, Drew V.

Riesbeck, Christopher K.

Meehan, James R.

Psychology Press

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