Best Artificial 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. 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).
Reviews
Find Best Price at Amazon"Even though I come from a strong theoretical background, I have to say one must do hands on tinkering to be able to solve one's own problem successfully. There are pieces of information hard to find somewhere else, and I have spent hundreds to thousands to attend workshops. I was hoping Keras, a high level api that enables fast experiments, is covered."
"I got this book for the deep learning portion (about half of the overall book length), and was shocked at the clarity of the conceptual explanations and code implementations."
"I'm only giving it four stars because despite the content itself being great, the print does have some issues like missing diagrams (see attached pictures)."
"The choice to start with Scikit-Learn was interesting, but makes sense on some level while he's introducing the more basic machine learning concepts. Simple machine learning techniques like logistic regression, data conditioning, dealing with training, validation, test set. Straightforward setup instructions, pretty intelligible explanation of the basic concepts (variables, placeholders, layers, etc.). The example code is quite good, and the notebooks are quite complete and seem to work well, with maybe a few tweaks and additional setup for some. Even just having a section on reinforcement learning is very rare in a book of this style, and Geron's samples and explanations are really solid."
"As with most technical books, it depends on where in the learning curve you are."
New York Times Best Seller How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology—and there’s nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who’s helped mainstream research on how to keep AI beneficial. “Original, accessible, and provocative….Tegmark successfully gives clarity to the many faces of AI, creating a highly readable book that complements The Second Machine Age ’s economic perspective on the near-term implications of recent accomplishments in AI and the more detailed analysis of how we might get from where we are today to AGI and even the superhuman AI in Superintelligence …. Enjoy the ride, and you will come out the other end with a greater appreciation of where people might take technology and themselves in the years ahead.” —Science “This is a compelling guide to the challenges and choices in our quest for a great future of life, intelligence and consciousness—on Earth and beyond.” —Elon Musk, Founder, CEO and CTO of SpaceX and co-founder and CEO of Tesla Motors “All of us—not only scientists, industrialists and generals—should ask ourselves what can we do now to improve the chances of reaping the benefits of future AI and avoiding the risks. This is the most important conversation of our time, and Tegmark’s thought-provoking book will help you join it.” —Professor Stephen Hawking, Director of Research, Cambridge Centre for Theoretical Cosmology “Tegmark’s new book is a deeply thoughtful guide to the most important conversation of our time, about how to create a benevolent future civilization as we merge our biological thinking with an even greater intelligence of our own creation.” —Ray Kurzweil, Inventor, Author and Futurist, author of The Singularity is Near and How to Create a Mind. “Being an eminent physicist and the leader of the Future of Life Institute has given Max Tegmark a unique vantage point from which to give the reader an inside scoop on the most important issue of our time, in a way that is approachable without being dumbed down.” —Jaan Tallinn, co-founder of Skype “This is an exhilarating book that will change the way we think about AI, intelligence, and the future of humanity.” —Bart Selman, Professor of Computer Science, Cornell University “The unprecedented power unleashed by artificial intelligence means the next decade could be humanity’s best—or worst. Though the topics he covers—AI, cosmology, values, even the nature of conscious experience—can be fairly challenging, he presents them in an unintimidating manner that invites the reader to form her own opinions.” —Nick Bostrom, Founder of Oxford’s Future of Humanity Institute, author of Superintelligence. "Tegmark’s book, along with Nick Bostrom’s Superintelligence, stands out among the current books about our possible AI futures....Tegmark explains brilliantly many concepts in fields from computing to cosmology, writes with intellectual modesty and subtlety, does the reader the important service of defining his terms clearly, and rightly pays homage to the creative minds of science-fiction writers who were, of course, addressing these kinds of questions more than half a century ago.
Reviews
Find Best Price at Amazon"Much more quickly than many anticipated, machine learning (a subset of AI) systems have defeated the best human Go players, are piloting self-driving cars, usefully if imperfectly translating documents, labeling your photos, understanding your speech, and so on. But he’s also got a lifetime of experience thinking carefully, rigorously, generally (and entertainingly to boot) about the “big picture” of what is possible, and what is not, over long timescales and cosmic distances (see his last book!). Finally, he's played an active and very key role (as you can read about in the book’s epilogue) in actually creating conversation and research about the impacts and safety of AI in the long-term. Chapter 1 lays out why AI is suddenly on everyone’s radar, and very likely to be extremely important over the coming decades, situating present-day as a crucial point within the wider sweep of human and evolutionary history on Earth. This raises a lot of rich, important, and extremely difficult questions that not that many people have thought through carefully (another in-print example is the excellent book by Bostrom). Chapter 6 exhibits Tegmark’s unique talent for tackling the big questions, looking at the *ultimate* limits and promise of intelligent life in the universe, and how stupefyingly high the stakes might be fore getting the next few decades right. (And I should also mention the prologue, which gives an fictional but less *science*fictional depiction of an artificial superintelligence being used by a small group to seize control of human society. It’s possible that real, general artificial intelligence (AGI) is 100 or more years away, a problem for the next generation, with large but manageable effects of “narrow” AI to deal with over a span of decades."
"Tegmark covers the spectrum of physics, cosmology, and artificial intelligence with the clarity and enthusiasm I haven’t witnessed since we were all glued to our televisions in the 1980s watching Carl Sagan unwrap the mysteries of the cosmos. Max Tegmark, a professor at MIT, is brilliant, creative, and rational, giving him that rare ability to explain the complex and mind-boggling to the rest of us. The primary purpose of the book, in Tegmark’s words, is to invite all of us to participate in setting goals for the development of artificial intelligence and, indeed, the future of scientific inquiry. As Tegmark clearly notes, there is no consensus in the AI community as to when, if ever, an intelligent machine capable of both learning and improving it’s own physical structure and performance, his definition of Life 3.0, will be created. Once it comes into existence, however, he makes a very convincing case that it will be too late to start thinking about aligning the machine’s goals with our own. Because language itself is a human convention that we invented, I am naturally skeptical of any written or oral explanation of anything that claims to be final and complete. I do feel, however, that he is sincerely receptive to dialogue—even insistent on it—and that makes him the voice we need to move forward in our pursuit of understanding and the wonders, like AI, that knowledge will put at our doorstep."
It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. (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.
Reviews
Find Best Price at Amazon"Because this book also makes very clear - is completely honest - that neural networks are a 'folk' technology (though they do not use those words): Neural networks work (in fact they work unbelievably well - at least, as Geoffrey Hinton himself has remarked, given unbelievably powerful computers), but the underlying theory is very limited and there is no reason to think that it will become less limited, and the lack of a theory means that there is no convincing 'gradient', to use an appropriate metaphor, for future development."
"I am a software developer and I bought this book to get exposure to deep learning."
"The text does a very good job of informing the reader what has been done and was is being done for neural networks."
"I am surprised by how poorly written this book is. I do not wish to speculate on the reason for this but it does sometimes does occur with. a first book in an important area or when dealing with pioneer authors with a cult following. More than half of this book reads like a bibliographic notes section of a book, and the authors seem. to be have no understanding of the didactic intention of a textbook (beyond a collation or importance sampling. of various topics). If you don't know linear algebra already, you cannot really hope to follow. anything (especially in the way the book is written). As a practical matter, Part I of the book is mostly redundant/off-topic for a neural network book. (containing linear algebra, probability, and so on). and Part III is written in a superficial way--so only a third of the book is remotely useful. It is understood that any machine learning book would have some mathematical sophistication, but the. main problem is caused by a lack of concern on part of the authors in promoting readability and an inability to. put themselves in reader shoes (surprisingly enough, some defensive responses to negative reviews tend to place. blame on math-phobic readers). A large part of the book (including restricted Boltzmann machines). is so tightly integrated with Probabilistic Graphical models (PGM), so that it loses its neural network focus. This portion is also in the latter part of the book that is written in a rather superficial way and. therefore it implicitly creates another prerequisite of being very used to PGM (sort-of knowing it wouldn't be enough). On the other hand, the PGM-heavy approach implicitly. increases the pre-requisites to include an even more advanced machine learning topic than neural networks. (with a 1200+ page book of its own). The book is an example of the fact that a first book in an important area with the name of. a pioneer author in it is not necessarily a qualification for being considered a good book."
"So…). If it’s for the people who want to get started with deep learning, it’s completely off topic, since it presents the mathematical nitty-gritty of the deep learning algorithms without mentioning any specifics of how to train a convo-net for example. If you’re really interested in Math behind Deep Learning out of curiousity (perhaps you’re a mathematician who wants to know what this deep learning thing is all about) perhaps this is a book for you."
Best Artificial Intelligence Expert Systems
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.
Reviews
Find Best Price at Amazon"Other books describe the difference between supervised and unsupervised learning, but this book goes further in describing how, say, decisions trees, support vector machines and deep neural networks fit compared to each other and within which subfields statistics play a larger role than others. The book also puts many techniques in historical perspective that I found very helpful, such as the rise, fall and rise again of deep neural networks with support vector machines taking a lead as the hottest technique in between (while also making clear that SVMs are a useful technique with unique strengths today)."
"Helped me put the subject into a broad perspective seeing how different aspects relate to each other."
"Good to read for both ML experts or newbies."
"This is a great book!"
"This is a nice book that gives you a glimpse of what is AI and how is being used in society."
"Great book for helping me "refresh" my knowledge of AI from when I got an advanced degree in Computer Science -- many years ago."
"Excellent overview of machine learning."
"So much of our lives are being controlled by algorithms; its key to understand what makes them tick so as to stay in control of my choices."
Best Computer Science
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).
Reviews
Find Best Price at Amazon"Even though I come from a strong theoretical background, I have to say one must do hands on tinkering to be able to solve one's own problem successfully. There are pieces of information hard to find somewhere else, and I have spent hundreds to thousands to attend workshops. I was hoping Keras, a high level api that enables fast experiments, is covered."
"I got this book for the deep learning portion (about half of the overall book length), and was shocked at the clarity of the conceptual explanations and code implementations."
"I'm only giving it four stars because despite the content itself being great, the print does have some issues like missing diagrams (see attached pictures)."
"The choice to start with Scikit-Learn was interesting, but makes sense on some level while he's introducing the more basic machine learning concepts. Simple machine learning techniques like logistic regression, data conditioning, dealing with training, validation, test set. Straightforward setup instructions, pretty intelligible explanation of the basic concepts (variables, placeholders, layers, etc.). The example code is quite good, and the notebooks are quite complete and seem to work well, with maybe a few tweaks and additional setup for some. Even just having a section on reinforcement learning is very rare in a book of this style, and Geron's samples and explanations are really solid."
"As with most technical books, it depends on where in the learning curve you are."
Best Mechanical Engineering
Digital technologies—with hardware, software, and networks at their core—will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape. “Offers important insights into how digital technologies are transforming our economy, a process that has only just begun.”. - Reid Hoffman, cofounder/chairman of LinkedIn and coauthor of the #1 New York Times bestseller The Start-up of You. I’ll encourage all of our entrepreneurs to read it, and hope their competitors don’t.”. - Marc Andreessen, cofounder of Netscape and Andreessen Horowitz. Long after the financial crisis and great recession have receded, the issues raised in this important book will be central to our lives and our politics.”. - Lawrence H. Summers, Charles W. Eliot University Professor at Harvard University.
Reviews
Find Best Price at Amazon"The Second Machine Age gives many examples of specific technologies like robots, AI and autonomous cars, and also lots of data showing how the economy is being transformed. The authors also make a strong argument that the way economists measure things, especially in terms of GDP, no longer does a good job of capturing what prosperity really means in the information age. There are lots of policy suggestions including reforming education to pay teachers more but also make them accountable, jump starting entrepreneurship, better job matching technologies, investing more in basic scientific research, upgrading national infrastructure, expanding skilled immigration, implementing smarter taxes, expanding the earned income tax credit (EITC), etc."
"There'll be nothing earth-shattering here for readers who follow technology trends or even who read WIRED magazine, but the book looks at all these things through a somewhat different lens (its impact on human work) than the tech press usually does, and I didn't find myself skimming even when they were covering developments with which I'm already very familiar. Their short-term prescriptions are sensible enough (basically: take steps to encourage general economic growth) but, as the authors themselves point out, these won't address the underlying problem, identified by Keynes among others, of technological change outpacing the ability of large segments of the workforce to retrain for new jobs."
Best Biotechnology
**From the author of the phenomenal million copy bestseller Sapiens **. **The Sunday Times # 1 bestseller**. While Sapiens looked back at our evolutionary development, this new book examines where we might be headed ( Homo Deus is subtitled “A Brief History of Tomorrow”). His innovative new book blends science, history and philosophy to explore the future of humanity in the face of artificial intelligence and examine whether our species will be rendered completely redundant.” – Cambridge Network “Spellbinding. “ It’s a chilling prospect, but the AI we’ve created could transform human nature, argues this spellbinding new book by the author of Sapiens .” – The Guardian. “Nominally a historian, Harari is in fact an intellectual magpie who has plucked theories and data from many disciplines — including philosophy, theology, computer science and biology — to produce a brilliantly original, thought-provoking and important study of where mankind is heading.” – Evening Standard. He’s opened a portal for us to contemplate on what kind of relationships we are forming with our data-crunching machines and whether ‘right’ must be determined by empirical evidence or good old ‘gut instinct.’” – The Hindu “[Harari’s] propositions are well-developed, drawing upon a combination of science, philosophy and history. While the book offers a rather pessimistic and even nihilistic view of man’s future, it is written with wit and style and makes compelling reading.” – iNews.
Reviews
Find Best Price at Amazon"Yuval Noah Harari's "Homo Deus" continues the tradition introduced in his previous book "Sapiens": clever, clear and humorous writing, intelligent analogies and a remarkable sweep through human history, culture, intellect and technology. He starts with exploring the three main causes of human misery through the ages - disease, starvation and war - and talks extensively about how improved technological development, liberal political and cultural institutions and economic freedom have led to very significant declines in each of these maladies. Continuing his theme from "Sapiens", a major part of the discussion is devoted to shared zeitgeists like religion and other forms of belief that, notwithstanding some of their pernicious effects, can unify a remarkably large number of people across the world in striving together for humanity's betterment. As in "Sapiens", Mr. Harari enlivens his discussion with popular analogies from current culture ranging from McDonald's and modern marriage to American politics and pop music. Mr. Harari's basic take is that science and technology combined with a shared sense of morality have created a solid liberal framework around the world that puts individual rights front and center. Ranging from dating to medical diagnosis, from the care of the elderly to household work, entire industries now stand to both benefit and be complemented or even superseded by the march of the machines. For reading more about these aspects, I would recommend books like Nick Bostrom's "Superintelligence", Pedro Domingos's "The Master Algorithm" and John Markoff's "Machines of Loving Grace". As a proficient prognosticator Mr. Harari's crystal ball remains murky, but as a surveyor of past human accomplishments his robust and unique abilities are still impressive and worth admiring."
"And he claims that humanism believes that individuals always know best about their own needs (when in fact, many have emphasized the importance of education in our development--he does not even reference John Dewey). For most of the book, Harari appears to be adopting a materialistic perspective, and one which is also extremely unsentimental and discounts the significance of human morale and character. He also discusses how animals and people have consciousness and subjective experiences, and presumes that artificial intelligence will remain unconscious (the "weak AI" hypothesis of John Searle). And on the very last page, he makes us wonder if his hardcore materialistic perspective has just been a long, extended ruse: he asks us to question a worldview that would deny the significance of consciousness. So it seems likely that in a future book he will focus on the nature of consciousness, and argue for non-theistic Buddhism (an understated agenda in Harari's writing--perhaps he thinks that this is the way for humanity to avoid the grim fate predicted here?). The comment begins with "Harari indeed believes that developing an understanding of consciousness, a science of mind, or however else one wishes to phrase it is the best and perhaps the only way to avert the grim fate that threatens humanity in this century."
Best Machine Theory
In CODE, they show us the ingenious ways we manipulate language and invent new means of communicating with each other. Charles Petzold's latest book, Code: The Hidden Language of Computer Hardware and Software , crosses over into general-interest nonfiction from his usual programming genre. From Louis Braille's development of his eponymous raised-dot code to Intel Corporation's release of its early microprocessors, Petzold presents stories of people trying to communicate with (and by means of) mechanical and electrical devices. The real value of Code is in its explanation of technologies that have been obscured for years behind fancy user interfaces and programming environments, which, in the name of rapid application development, insulate the programmer from the machine.
Reviews
Find Best Price at Amazon"For a reader like me, who asked every teacher from elementary school through college "why do we count to 10" and clung to the best answer of "it's arbitrary - it's just how it's always been done" until reading this book (and who struggled to convert binary to base ten), this book was gold."
"Added as an addition to my computer library."
"I just finished this book and got way more out of it than I expected."
"It is not meant to be intensive and, for that reason, I would not recommend this to anyone as a "supplementary book" for a digital design class but rather a concise introduction for a young, curious mind."
"Before I read this book, I already knew about logic gates, but I did not know (1) how electric and electonic devices can in the real world perform the function of logic gates and (2) how by arranging logic gates wisely one can perform addition and subtraction and (3) more complicated mathematical operations can be performed by doing "a lot of" additions and subtractions. But overall I think I have learned a lot from this book."
"This book takes a look at the most basic building blocks of modern technology."
"This book is a really great book."
"However, lately, there are still several books that do better job if you really want to learn more about machines."
Best Robotics & Automation
Digital technologies—with hardware, software, and networks at their core—will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape. “Offers important insights into how digital technologies are transforming our economy, a process that has only just begun.”. - Reid Hoffman, cofounder/chairman of LinkedIn and coauthor of the #1 New York Times bestseller The Start-up of You. I’ll encourage all of our entrepreneurs to read it, and hope their competitors don’t.”. - Marc Andreessen, cofounder of Netscape and Andreessen Horowitz. Long after the financial crisis and great recession have receded, the issues raised in this important book will be central to our lives and our politics.”. - Lawrence H. Summers, Charles W. Eliot University Professor at Harvard University.
Reviews
Find Best Price at Amazon"The Second Machine Age gives many examples of specific technologies like robots, AI and autonomous cars, and also lots of data showing how the economy is being transformed. The authors also make a strong argument that the way economists measure things, especially in terms of GDP, no longer does a good job of capturing what prosperity really means in the information age. There are lots of policy suggestions including reforming education to pay teachers more but also make them accountable, jump starting entrepreneurship, better job matching technologies, investing more in basic scientific research, upgrading national infrastructure, expanding skilled immigration, implementing smarter taxes, expanding the earned income tax credit (EITC), etc."
"There'll be nothing earth-shattering here for readers who follow technology trends or even who read WIRED magazine, but the book looks at all these things through a somewhat different lens (its impact on human work) than the tech press usually does, and I didn't find myself skimming even when they were covering developments with which I'm already very familiar. Their short-term prescriptions are sensible enough (basically: take steps to encourage general economic growth) but, as the authors themselves point out, these won't address the underlying problem, identified by Keynes among others, of technological change outpacing the ability of large segments of the workforce to retrain for new jobs."
Best Computing, Internet & Digital Media in Portuguese
Gestão e Governança de Dados: Promovendo dados como ativo de valor nas empresas (Portuguese Edition)
Entre os assuntos abordados destacamos: Conceitos básicos de Gestão de Dados; Papéis, responsabilidades e formas de estruturação da disciplina Gestão de Dados nas empresas; Conceitos básicos sobre Big Data; Governança de Dados; Visão geral do guia DAMA-DMBOK®; Modelagem de Dados; Arquitetura de Dados; Gestão de Dados Mestres e Referência; Qualidade de Dados; Gestão de Dados Moderna e suas boas práticas; Desenvolvimento profissional e informações básicas sobre as certificações da área.
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