Best Natural Language Processing
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.
Reviews
Find Best Price at Amazon"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."
"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."
"Great book, takes a while to get going, but shows some excellent uses of scikit learn."
"Book content is very up-to-date and offers great hands-on experience."
"Great Book, well explained a lot of good examples, one of those books that the more times you read it , the more you profit from it."
"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."
The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. This landmark book provides a very extensive coverage of the field, ranging from basic representational issues to the latest techniques for approximate inference and learning.
Reviews
Find Best Price at Amazon"I love this book."
"A great review of the book from Kevin Murphy appeared in Artificial Intelligence Journal."
"Fantastic....great text."
"Perfect book, very detailed and very readable with lots of real world examples ... Of course reading it takes time ... not for faint heart ..."
"The book integrates several ideas into a well defined concept."
"Very complete reference on the subject."
"This is the book that I'm looking for."
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.
Reviews
Find Best Price at Amazon"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."
"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."
"Great book, takes a while to get going, but shows some excellent uses of scikit learn."
"Book content is very up-to-date and offers great hands-on experience."
"Great Book, well explained a lot of good examples, one of those books that the more times you read it , the more you profit from it."
"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 Computer Neural Networks
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."
"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 Computer Programming
Early in his software developer career, John Sonmez discovered that technical knowledge alone isn't enough to break through to the next income level - developers need "soft skills" like the ability to learn new technologies just in time, communicate clearly with management and consulting clients, negotiate a fair hourly rate, and unite teammates and coworkers in working toward a common goal. Plus how helping your manager with his goals can make you the MVP of your team The technical skills that every professional developer must have - but no one teaches you (most developers are missing some critical pieces, they don't teach this stuff in college, you're expected to just "know" this) An inside look at the recruiting industry. Brand New Developers In this book you'll discover what it's like to be a professional software developer, how to go from "I know some code" to possessing the skills to work on a development team, how to speed along your learning by avoiding common beginner traps, and how to decide whether you should invest in a programming degree or "bootcamp." Not Just For Beginners--Guaranteed To Make You A Better Developer When I first started reading this book I was skeptical. Rui FigueiredoSoftware Developer and Computer Science PhDDublin, Ireland Deals With The Human Side Of Software Development. This book is different from all other software development books I have read because it deals with the human side of software development. Even though as software developers we are surrounded with the latest technology, we are still people with feelings, fears and dreams, and John's book focuses on that. The Complete Software Developer's Career Guide is a great resource that I wish that I had years earlier in my career and in my education. Fernando Z.Senior Software Developer, Blogger and Programming FanaticCentral Texas Get It Even If You're NOT In Software Development. Invaluable advice for any software developer, from entry-level to senior. John Sonmez is a software developer and the author of two bestselling books, The Complete Software Developer's Career Guide and Soft Skills: The Software Developer's Life Manual. He's also the founder of the Simple Programmer blog and YouTube channel, where he reaches 1.4 million software developers yearly and helps them develop the unique blend of skills that made him a highly paid, highly sought-after developer and consultant.
Reviews
Find Best Price at Amazon"The book meets the breadth and depth requirements one would expect of a software developers' career guide. I wish I had a book like this when I was starting out as a software developer back in the days."
"It is a combination of technical advise with personal advise addressed to software developers, and being one for more than 20 years, I can say that it is something that we all should know, but it is not as clear as we think until we see somebody writing this or telling you these."
"The Complete Software Developer's Career Guide continues on the path blazed by Sonmez's Soft Skills of taking the complexity in our life (and in this book's case, your software engineering career) and breaking it down into manageable & actionable chunks."
"I first heard of John Sonmez from his Soft Skills book."
"Well much of the information you can get online, but if you prefer to know software career development by reading book, then this is a good book."
"I read this book as John was writing it."
"I only finished one third of Johns newest books but I can already wholeheartedly recommend it to every software developer who wanna improve is career of life in general."
"I've been following this author for some time, and have been using some of his materials to advance my career."
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.
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."
"The text does a very good job of informing the reader what has been done and was is being done for neural networks."
"Excellent overview."
"Great (and a very timely & relevant) book on this exciting and cutting edge domain."
"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 Business Software
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 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."
Best Artificial Intelligence
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. “Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics. … the book also demonstrates how to apply these methods using various R packages by providing detailed worked examples using interesting real data applications.” (Klaus Nordhausen, International Statistical Review, Vol. “The book is structured in ten chapters covering tools for modeling and mining of complex real life data sets. … The style is suitable for undergraduates and researchers … and the understanding of concepts is facilitated by the exercises, both practical and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. "The book excels in providing the theoretical and mathematical basis for machine learning, and now at long last, a practical view with the inclusion of R programming examples.
Reviews
Find Best Price at Amazon"This is a wonderful book written by luminaries in the field."
"The book provides the right amount of theory and practice, unlike the earlier (venerable and, by now, stable) text authored (partly) by the last two authors of this one (Elements of Statistical Learning), which was/is a little heavy on the theoretical side (at least for practitioners without a strong mathematical background). It is, however, an excellent introduction to Learning due to the ability of the authors to strike a perfect balance between theory and practice. ISL is an excellent choice for a two-semester advanced undergraduate (or early graduate) course, practitioners trained in classical statistics who want to enter the Learning space, and seasoned Machine Learners. ____________________________________________. UPDATE (12/17/2013): Two of the authors (Hastie & Tibshirani) are offering a 10-week free online course (StatLearning: Statistical Learning) based on this book found at Stanford University's Web site (Starting Jan. 21, 2014)."
"Hands down one of the best intro books to data science/machine learning out there."
"I came to this book after a few other more technical and comprehensive books on machine learning and still find this book a useful and interesting read."
"Comparable to Mitchell's "Machine Learning" only more up to date and includes hands-on labs (using R... well, better than nothing... had they used something like numpy/python, 5-stars!)."
"I am taking off one star as this book does not cover naive Bayes which is a very useful and popular algorithm."
"Great book."
"A beautifully written and composed survey of modern statistical learning techniques."
Best Databases & Big Data
If you’ve read it before, you’ll rediscover what made Don’t Make Me Think so essential to Web designers and developers around the world. Steve Krug (pronounced "kroog") is best known as the author of Don't Make Me Think: A Common Sense Approach to Web Usability , now in its second edition with over 350,000 copies in print.
Reviews
Find Best Price at Amazon"As the subtitle, "A Common Sense Approach", and Krug's consultancy's name, "Advanced Common Sense" ([...]) convey, many great design considerations today involve some simple approaches to dramatically improve your web user experiences."
"This should be required reading by ALL marketing execs, ALL web designers and developers, ALL graphic designers, ALL product designers and inventors, and ALL copywriters."
"Krug breaks the issues of usable web design into simple, digestible form for anyone who depends on a website for promotion or income."
"That fact makes this book a good read for anybody who is involved in any aspect of a websites operation, Web Developer, Marketing, Sales and Management (from mid level to upper level)."
"Many of the ideas and resources in the book have been incredibly valuable to me in my work, and it's almost always the first book I recommend to anyone asking questions about User Experience related topics."
"Unlike a boring text book, Krug makes the book really fun with helpful (yet obvious) examples that bring some concepts to life."
"This book was on the "suggested" reading lists for an interface design class that I took in college."
"But how is though to follow common sense when building a website! I use it on my daily work, to guide my business partners in the construction of the websites for my B2B Clients, and after reading it, is quirte amazing how you can immediatelly spot in the web the websites that follow its principles and those that don't."
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 Software
If you’ve read it before, you’ll rediscover what made Don’t Make Me Think so essential to Web designers and developers around the world. Steve Krug (pronounced "kroog") is best known as the author of Don't Make Me Think: A Common Sense Approach to Web Usability , now in its second edition with over 350,000 copies in print.
Reviews
Find Best Price at Amazon"As the subtitle, "A Common Sense Approach", and Krug's consultancy's name, "Advanced Common Sense" ([...]) convey, many great design considerations today involve some simple approaches to dramatically improve your web user experiences."
"This should be required reading by ALL marketing execs, ALL web designers and developers, ALL graphic designers, ALL product designers and inventors, and ALL copywriters."
"Krug breaks the issues of usable web design into simple, digestible form for anyone who depends on a website for promotion or income."
"That fact makes this book a good read for anybody who is involved in any aspect of a websites operation, Web Developer, Marketing, Sales and Management (from mid level to upper level)."
"Many of the ideas and resources in the book have been incredibly valuable to me in my work, and it's almost always the first book I recommend to anyone asking questions about User Experience related topics."
"Unlike a boring text book, Krug makes the book really fun with helpful (yet obvious) examples that bring some concepts to life."
"This book was on the "suggested" reading lists for an interface design class that I took in college."
"But how is though to follow common sense when building a website! I use it on my daily work, to guide my business partners in the construction of the websites for my B2B Clients, and after reading it, is quirte amazing how you can immediatelly spot in the web the websites that follow its principles and those that don't."
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."