Best Artificial Intelligence & Semantics
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.
Reviews
Find Best Price at Amazon"Some of the topics explored: artificial intelligence, cognitive science, mathematics, programming, consciousness, zen, philosophy, linguistics, neuroscience, genetics, physics, music, art, logic, infinity, paradox, self-similarity. Inbetween chapters, he switches to a dialogue format between fantasy characters; here he plays with the ideas being discussed, and performs postmodern literary experiments. GEB combines the playful spirit of Lewis Carroll, the labyrinthine madness of Borges, the structural perfectionism of Joyce, the elegant beauty of mathematics, and the quintessential fascination of mind, all under one roof. The task of reducing mind to math, of connecting the nature of consciousness to an idea in formal systems, is such a lofty goal, that even if true, the author could never rigorously prove this thesis, only approach it from every conceivable direction. In the grand line of reductionism, where we in theory reduce consciousness to cognitive science to neuroscience to biology to chemistry to physics to math to metamath, GEB positions itself at the wraparound point at unsigned infinity, where the opposite ends of the spectrum meet."
"There is sooo much content in this book it's going to take my whole life to even begin to understand."
"For those of you who want to know about how things are this is a must read."
"So far a fantastic book."
"If you are interested in fractals, improbable harmonies, math recursion, puzzles, artistic illusionary impossibilities and strange loopy weirdness where life seems to look back at itself."
"Book in great shape."
"Condition of book was good, not great, slightly worse than described but totally acceptable."
"arrived safe and sound."
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."
"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."
"I've read many other books on Machine Learning but they are too academic (other authors often times omit practical issues like complexity of the algorithms, etc.)."
"However, the book I purchased and filled by amazon has the entire Table of content and preface missing."
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."
"Very clear exposition, does the math without getting lost in the details."
"Great book."
"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 Office Automation Business
You will discover everything you need to know about social media marketing This expanded 3rd edition has Outstanding New Content to make building your online presence even more powerful than before! Doubling Your Instagram Followers Understanding Facebook Ad marketing Top Strategies For Growth Monetizing Your Social Media.
Reviews
Find Best Price at Amazon"As someone who has used social media to support multilple activities this book is just so helpful. There are so many social media formats out there now and this book helps break them down and what their application for different use would be."
"If you really are completely new to social media or struggling to keep up it’s a quick read that will tell you the strategies of each of the major social media sites and a step by step how to set up each."
"Many social media platforms are explained and each section has a few suggestions on utilizing it efficiently."
"I have a facebook page and even a website but short of paying all the time to boost my posts, I was wondering what I could do to advertise and get more people interested in my brand."
"He tells how to recognize your brand, your key audience- customers, and even points out a few social media sites I hadn't heard of before and was interested in checking out."
"Social Media: Strategies to Mastering Your Brand is a useful guide to teaching businesses how to use social media as resort for marketing their business."
"The information is fit for those who just want to understand the different social media platforms and establish a presence, as well as, those who want to invest more time, money and effort in order to maximize their results."
Best Prolog Programming
An air fryer will roast, steam, bake and grill all of the food for you and the family. In this book we gathered only the best 530 recipes for Air Fryer for: • Breakfast • Lunch • Sides • Desserts • Meat • Poultry • Seafood • Snacks It’s time to make a change in the way you cook.
Reviews
Find Best Price at Amazon"the choice of recipes is very large and it's very cool."
"It helps me to understand and learn how to utilize my new Air Fryer much better without worrying about calorie intake and the negative sides of taking fried food."
"I have tried many of these recipes and all the recipes are very easy to prepare and very tasty."
"I prefer only healthy food and in this book the number of options for preparing such dishes is amazing."
"The book has information on how air fryer cookbook recipes work to help you cook very tasty in this way."
"The author gives us 530 delicious and simple recipes "Fricker" in this kitchen."
"I am an addict in using air fryer for I can roast, steam, bake and grill with it on whatever foods I need for my family."
"This Air Fryer book offers recipes and The book starts off with great tips and in information about the Air Fryer Cookbook and its benefits."
Best Lisp Programming
A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. Abelson and Sussman's classic Structure and Interpretation of Computer Programs teaches readers how to program by employing the tools of abstraction and modularity. The book spends a great deal of time considering both this decomposition and the process of knitting the smaller pieces back together.
Reviews
Find Best Price at Amazon"It was first exposure to a language other than BASIC, so I acknowledge the bar was pretty low, but I was blown away."
"Great book covering LISP, lambda calculus, and the meta-circular interpreter."
"A must read for anyone in programming."
"You may have to search a bit to find a vintage Scheme compatible with the exercises used in the book - people on the Internet (particularly the scheme community) are very helpful here."
"I wish I would have read this book 10 years back..."
"I wouldn't recommend it to someone with no prior computer science skills unless they are looking to become a computer scientist."
"It used to be offered at MIT as 6.001 but unfortunately they removed it in favor of 6.01 Python."
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 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."
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.
Reviews
Find Best Price at Amazon"Depending on on how you think of programming, this book could be incredibly insightful, or horribly abstract and impractical."
"I find it very interesting and it goes into details for design patterns and re-use of code."
"I have been using this book as a reference on Design Pattern."
"This book will forever stand as a foundation of software development."
"OK, so this title has become almost a bible for the software industry - it seems to get cited by every other author I read, so I thought it was about time I actually bought a copy."
"Even though I program in ABAP, it helps me to translate the pattern into that code."
"Great book for who want to understand each pattern deeply."
"Excelent book."
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 Machine Theory
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.
Reviews
Find Best Price at Amazon"Some of the topics explored: artificial intelligence, cognitive science, mathematics, programming, consciousness, zen, philosophy, linguistics, neuroscience, genetics, physics, music, art, logic, infinity, paradox, self-similarity. Inbetween chapters, he switches to a dialogue format between fantasy characters; here he plays with the ideas being discussed, and performs postmodern literary experiments. GEB combines the playful spirit of Lewis Carroll, the labyrinthine madness of Borges, the structural perfectionism of Joyce, the elegant beauty of mathematics, and the quintessential fascination of mind, all under one roof. The task of reducing mind to math, of connecting the nature of consciousness to an idea in formal systems, is such a lofty goal, that even if true, the author could never rigorously prove this thesis, only approach it from every conceivable direction. In the grand line of reductionism, where we in theory reduce consciousness to cognitive science to neuroscience to biology to chemistry to physics to math to metamath, GEB positions itself at the wraparound point at unsigned infinity, where the opposite ends of the spectrum meet."
"There is sooo much content in this book it's going to take my whole life to even begin to understand."
"For those of you who want to know about how things are this is a must read."
"So far a fantastic book."
"If you are interested in fractals, improbable harmonies, math recursion, puzzles, artistic illusionary impossibilities and strange loopy weirdness where life seems to look back at itself."
"Book in great shape."
"Condition of book was good, not great, slightly worse than described but totally acceptable."
"arrived safe and sound."