Science Books
Free and open online science books and textbooks.
27 listings
Submitted Dec 23, 2016 to Science Books There is already a fair number of book about numpy (see Bibliography) and a legitimate question is to wonder if another book is really necessary. As you may have guessed by reading these lines, my personal answer is yes, mostly because I think there's room for a different approach concentrating on the migration from Python to numpy through vectorization. There is a lot of techniques that you don't find in books and such techniques are mostly learned through experience. The goal of this book is to explain some of them and to make you acquire experience in the process.
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Submitted Dec 05, 2016 to Science Books Neural Networks and Deep Learning is a free online book. The book will teach you about:
- Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data - Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning. |
Submitted Dec 04, 2016 to Science Books Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth.
The subtitle of this book is "A Modern Approach.'' The intended meaning of this rather empty phrase is that we have tried to synthesize what is now known into a common framework, rather than trying to explain each subfield of AI in its own historical context. We apologize to those whose subfields are, as a result, less recognizable. This edition captures the changes in AI that have taken place since the last edition in 2003. |
Submitted Dec 04, 2016 to Science Books This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning. Available for download in PDF and ePub formats.
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Submitted Dec 03, 2016 (Edited Dec 03, 2016) to Science Books By John Winn and Christopher Bishop with Thomas Diethe
In this book we look at machine learning from a fresh perspective which we call model-based machine learning. This viewpoint helps to address all of these challenges, and makes the process of creating effective machine learning solutions much more systematic. It is applicable to the full spectrum of machine learning techniques and application domains, and will help guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning. |
Submitted Nov 30, 2016 to Science Books The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.
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Submitted Feb 21, 2009 (Edited Jan 15, 2017) to Science Books An online textbook by Prof. David Stockburger, Missouri State University.
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