Best AI & Machine Learning

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.

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).

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.

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.

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.

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.

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.

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).

After an introduction to cryptography and data security, the authors explain the main techniques in modern cryptography, with chapters addressing stream ciphers, the Data Encryption Standard (DES) and 3DES, the Advanced Encryption Standard (AES), block ciphers, the RSA cryptosystem, public-key cryptosystems based on the discrete logarithm problem, elliptic-curve cryptography (ECC), digital signatures, hash functions, Message Authentication Codes (MACs), and methods for key establishment, including certificates and public-key infrastructure (PKI). Prof. Paar has taught cryptography for 15 years to engineering and computer science students in the US and in Europe, and he has taught many industrial practitioners at organizations such as Motorola, Philips and NASA. He has published extensively about his theoretical and industrial work through leading international conferences and journals, and he has taught many IT security and cryptography courses in industry.

In the years following her role as the lead author of the international bestseller, Limits to Growth ―the first book to show the consequences of unchecked growth on a finite planet― Donella Meadows remained a pioneer of environmental and social analysis until her untimely death in 2001. Just before her death, scientist, farmer and leading environmentalist Meadows (1941-2001) completed an updated, 30th anniversary edition of her influential 1972 environmental call to action, Limits to Growth , as well as a draft of this book, in which she explains the methodology-systems analysis-she used in her ground-breaking work, and how it can be implemented for large-scale and individual problem solving. An invaluable companion piece to Limits to Growth , this is also a useful standalone overview of systems-based problem solving, "a simple book about a complex world" graced by the wisdom of a profound thinker committed to "shaping a better future. "Dana Meadows' exposition in this book exhibits a degree of clarity and simplicity that can only be attained by one who profoundly and honestly understands the subject at hand--in this case systems modeling. This is modestly called a primer, and indeed it is, but unlike most books with that title, this one quickly takes one from the elementary into deep systems thinking about issues as critical today as they were when Dana wrote these words. As the book moves from the 'mechanics' of systems dynamics to Dana's more philosophical perspective, we are treated to her inherent belief in human values that consider the good of all, and how much more effective considering the needs of others is likely to be in solving larger, complex problems. The universe and our society may be very complex and operate in counterintuitive, non-liner fashion, but following the insights of this book and applying them will provide for far more effective solutions to the challenges of a 7 billion person planet than current incremental, linear responses by governments, corporations and individuals." For her systems thinking included the expected things like recognizing patterns, connections, leverage points, feedback loops and also the human qualities of judgment, foresight, and kindness. To live sustainably on our planet, we must learn to understand human-environment interactions as complex systems marked by the impact of human actions, the prominence of nonlinear change, the importance of initial conditions, and the significance of emergent properties. "An inspiring sequel to Dana Meadows' lifetime of seminal contributions to systems thinking, this highly accessible book should be read by everyone concerned with the world's future and how we can make it as good as it possibly can be."

An inventive blend of autobiography, science writing, philosophy and advice, this book tells the wild story of his personal and professional life as a scientist, from his childhood in the UFO territory of New Mexico, to the loss of his mother, the founding of the first start-up, and finally becoming a world-renowned technological guru. His style is wonderfully discursive, reflecting his wide range of interests and experiences.”. ―Emily Parker, The Washington Post. “ Dawn of the New Everything spirits us back to a time when a plurality of ideas about what the Internet could be were still in play . “Jaron Lanier is both cheerleader and doomsayer in a highly personal story of virtual reality . a studied and nuanced interrogation of VR’s potential, as well as a gentle critique of what he sees as a failure of imagination when it comes to the medium’s current proponents.”. ― The Guardian. Integrating memoir, science writing, philosophical reflection, and down-to-earth advice, he reveals that virtual reality can clarify how the brain and the body connect to the world, giving us a deeper understanding of what it means to be human . This culturally significant title with its compelling personal narrative proves yet again that Lanier is a thinker whose work should be read and contemplated.”. ― Booklist. “Perhaps surprisingly for a book about the birth of virtual reality, this is a deeply human, highly personal, and beautifully told story .”. ―Dave Eggers, author of The Circle. It’s entirely unexpected and disarming to read about these concepts from an unabashedly subjective point of view. Not just for entertainment, but because Mr. Lanier has thoroughly convinced me that it’s the beginning of an enormous paradigm shift in the very way humans relate and communicate.”. ―Joseph Gordon-Levitt, actor and director. “The author is an evangelist for the good side of VR, which now offers insights into human perception and cognition that are forcing a radical re-evaluation of who we are. A spirited exploration of tech by a devotee who holds out the hope that bright things are just around the corner.”. ― Kirkus Reviews.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. Almost all of the chapters are revised.… The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.… If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven’t, then I still strongly recommend you have this book at your desk. … the book may also be of interest to a theoretically inclined reader looking for an entry point to the area and wanting to get an initial understanding of which mathematical issues are relevant in relation to practice. … this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d). You can flip the book open to any page, read a sentence or two and be hooked for the next hour or so.” (Peter Rabinovitch, The Mathematical Association of America, May, 2012).

Each chapter is relatively selfcontained and presents an algorithm, a design technique, an application area, or a related topic. The algorithms are described and designed in a manner to be readable by anyone who has done a little programming. "As an educator and researcher in the field of algorithms for over two decades, I can unequivocally say that the Cormen et al book is the best textbook that I have ever seen on this subject. "Introduction to Algorithms, the 'bible' of the field, is a comprehensive textbook covering the full spectrum of modern algorithms: from the fastest algorithms and data structures to polynomial-time algorithms for seemingly intractable problems, from classical algorithms in graph theory to special algorithms for string matching, computational geometry, and number theory.

By the time you finish this book, you’ll be able to take advantage of the best design practices and experiences of those who have fought the beast of software design and triumphed. Eric Freeman recently ended nearly a decade as a media company executive, having held the position of CTO of Disney Online & Disney.com at The Walt Disney Company. More recently, she's been a master trainer for Sun Microsystems, teaching Sun's Java instructors how to teach the latest technologies to customers, and a lead developer of several Sun certification exams.

You'll also learn essential building techniques like how to use beams, gears, and connector blocks effectively in your own designs. The EXPLOR3R, a wheeled vehicle that uses sensors to navigate around a room and follow lines The FORMULA EV3 RACE CAR, a streamlined remote-controlled race car ANTY, a six-legged walking creature that adapts its behavior to its surroundings SK3TCHBOT, a robot that lets you play games on the EV3 screen The SNATCH3R, a robotic arm that can autonomously find, grab, lift, and move the infrared beacon LAVA R3X, a humanoid robot that walks and talks. Each chapter contains several short challenges, dubbed "discoveries," which are cleverly accompanied by a legend: whimsical gear wheels represent the estimated amount of building time; tiny Microsoft Windows-esque blocks show the expected level of programming expertise; and a small clock estimates the length of time it should take to solve the challenge. The size, advanced vocabulary, and organization of the book evokes a science or physics textbook, which is warranted due to the amount of complex and detailed programming information contained within.