Best Computer Vision & Pattern Recognition

These texts cover the design of object-oriented software and examine how to investigate requirements, create solutions and then translate designs into code, showing developers how to make practical use of the most significant recent developments. Design Patterns is a modern classic in the literature of object-oriented development, offering timeless and elegant solutions to common problems in software design.

Capturing a wealth of experience about the design of object-oriented software, four top-notch designers present a catalog of simple and succinct solutions to commonly occurring design problems. Design Patterns is a modern classic in the literature of object-oriented development, offering timeless and elegant solutions to common problems in software design. It's a book of design patterns that describe simple and elegant solutions to specific problems in object-oriented software design....Once you understand the design patterns and have had an "Aha!"

It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts.

Optical Character Recognition: An Illustrated Guide to the Frontier will pique the interest of users and developers of OCR products and desktop scanners, as well as teachers and students of pattern recognition, artificial intelligence, and information retrieval. Optical Character Recognition: An Illustrated Guide to the Frontier is suitable as a secondary text for a graduate level course on pattern recognition, artificial intelligence, and information retrieval, and as a reference for researchers and practitioners in industry.

Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib'.Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion).

Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib'.Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion).

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. (Geoffrey Hinton FRS, Emeritus Professor, University of Toronto; Distinguished Research Scientist, Google). Deep learning has taken the world of technology by storm since the beginning of the decade. (Yann LeCun, Director of AI Research, Facebook; Silver Professor of Computer Science, Data Science, and Neuroscience, New York University). [T]he AI bible... the text should be mandatory reading by all data scientists and machine learning practitioners to get a proper foothold in this rapidly growing area of next-gen technology.

A reorganized and comprehensive major revision of a classic book, this edition provides a bridge between introductory digital communications and more advanced treatment of information theory. In 1970 the first author published a book entitled An Introduction to Error-Correcting Codes, which presented the fundamentals of the previous two decades of work covering both block and convolutional codes. Other major additions included a comprehensive treatment of the error-detecting capabilities of block codes and an emphasis on soft decoding methods for convolutional codes. Three of these new developments stand out in particular: the application of binary convolutional and block codes to expanded (nonbinary) modulation alphabets, the development of practical soft decoding methods for block codes, and the discovery of soft-input, soft-output iterative decoding techniques for block and convolutional codes. A total of seven new chapters covering these topics have been added to this edition: two chapters on trellis- and block-coded modulation techniques, three chapters on soft decoding methods for block codes, and two chapters on turbo and low-density parity-check codes and iterative decoding. Chapters 3 through 10 cover in detail the fundamentals of block codes. Chapter 8 provides detailed coverage of majority-logic decodable codes, including the important classes of Euclidean and projective geometry codes. Chapter 9 develops the theory of the trellis structure of block codes, laying the groundwork for the introduction of trellis-based soft decoding algorithms in Chapter 14. Chapter 10, written by Professor Marc Fossorier, presents comprehensive coverage of reliability-based soft decoding methods for block codes and includes an introduction to iterative decoding techniques. Convolutional codes are introduced in Chapter 11, with the encoder state diagram serving as the basis for studying code structure and distance properties. Chapter 12 covers optimum decoding methods for convolutional codes, with an emphasis on the (maximum likelihood) Viterbi decoding algorithm for both hard and soft demodulator decisions. Chapter 13 covers suboptimum decoding methods for convolutional codes, with an emphasis on sequential decoding, using both the ZJ (stack) and Fano algorithms, and majority-logic decoding. Chapter 14 extends the soft decoding methods introduced for convolutional codes in Chapter 12 to block codes. This completely new chapter makes extensive use of the block code trellis structures introduced in Chapter 9. Chapter 15 discusses the important concepts of code concatenation, multistage decoding, and code decomposition. Chapter 16 introduces the area of parallel concatenation, or turbo coding, and its related iterative decoding techniques based on the BCJR algorithm presented in Chapter 12. Both block (Chapter 20) and convolutional (Chapter 21) burst-error-correcting codes are included. As a text the book can be used as the basis for a two-semester sequence in coding theory, with Chapters 1-10 on the fundamentals of block codes covered in one semester and the remaining chapters on convolutional codes and advanced block code topics in a second semester. Another possibility is to cover Chapters 1-8 and 11-13, which include the fundamentals of both block and convolutional codes, in one semester, followed by a second semester devoted to advanced topics. A course on block codes comprise Chapters 1-7 plus selected topics from Chapters 8-10, 14-15, 17, 19-20, and 22, whereas Chapters 1, 11-13, 16, 18, and 21 provide a thorough coverage of convolutional codes.

These texts cover the design of object-oriented software and examine how to investigate requirements, create solutions and then translate designs into code, showing developers how to make practical use of the most significant recent developments. Design Patterns is a modern classic in the literature of object-oriented development, offering timeless and elegant solutions to common problems in software design.

However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it. Besides being a profound and entertaining meditation on human thought and creativity, this book looks at the surprising points of contact between the music of Bach, the artwork of Escher, and the mathematics of Gödel. Hofstadter's great achievement in Gödel, Escher, Bach was making abstruse mathematical topics (like undecidability, recursion, and 'strange loops') accessible and remarkably entertaining. Escher, Kurt Gödel: biographical information and work, artificial intelligence (AI) history and theories, strange loops and tangled hierarchies, formal and informal systems, number theory, form in mathematics, figure and ground, consistency, completeness, Euclidean and non-Euclidean geometry, recursive structures, theories of meaning, propositional calculus, typographical number theory, Zen and mathematics, levels of description and computers; theory of mind: neurons, minds and thoughts; undecidability; self-reference and self-representation; Turing test for machine intelligence.

Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details.

However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it. Besides being a profound and entertaining meditation on human thought and creativity, this book looks at the surprising points of contact between the music of Bach, the artwork of Escher, and the mathematics of Gödel. Hofstadter's great achievement in Gödel, Escher, Bach was making abstruse mathematical topics (like undecidability, recursion, and 'strange loops') accessible and remarkably entertaining. Escher, Kurt Gödel: biographical information and work, artificial intelligence (AI) history and theories, strange loops and tangled hierarchies, formal and informal systems, number theory, form in mathematics, figure and ground, consistency, completeness, Euclidean and non-Euclidean geometry, recursive structures, theories of meaning, propositional calculus, typographical number theory, Zen and mathematics, levels of description and computers; theory of mind: neurons, minds and thoughts; undecidability; self-reference and self-representation; Turing test for machine intelligence.

In The Master Algorithm , Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. "Pedro Domingos demystifies machine learning and shows how wondrous and exciting the future will be. "Domingos is the perfect tour guide from whom you will learn everything you need to know about this exciting field, and a surprising amount about science and philosophy as well. "[An] enthusiastic but not dumbed-down introduction to machine learning... lucid and consistently informative... With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself. "This book is a must have to learn machine learning without equation.