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Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks, by Jeff Heaton
Download PDF Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks, by Jeff Heaton
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Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.
- Sales Rank: #218986 in Books
- Published on: 2015-10-28
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .85" w x 7.50" l, 1.55 pounds
- Binding: Paperback
- 374 pages
About the Author
Jeff Heaton is a data scientist, PhD student and indy publisher. Specializing in Java, C#, C/C++, Python and R, he is an active technology blogger, open source contributor, and author of more than ten books. His areas of expertise include predictive modeling, data mining, big data, business intelligence, and artificial intelligence. Jeff holds a Master’s Degree in Information Management from Washington University, is a senior member of the IEEE and a PhD student at Nova Southeastern University in computer science. He is the lead developer for the Encog Machine Learning Framework open source project and a fellow of the Life Management Institute (FLMI).
Most helpful customer reviews
11 of 11 people found the following review helpful.
Useless for deep understanding, not good for beginners either
By Dmitri Kozlov
The book describes few concepts. A beginner would expect to see a bigger picture rather than a seemingly random set of methods or models. A professional would look for detailed explanation of concepts.The book provides neither. Descriptions are shallow, formulas contain errors, pseudo code is often wrong. Some description would better be replaced with concise and clear formulas, some formulas require explanations. The book needs a proof editing. However, the bigger problem is how the author presents the material.
I'd recommend a much better book to start with: "Neural Networks and Deep Learning" by Michael Nielsen.
5 of 5 people found the following review helpful.
... ran through the book quickly (kindle) and it seems good although I expected much more mathematics involved (Integrals
By Amazon Customer
I ran through the book quickly (kindle) and it seems good although I expected much more mathematics involved (Integrals, derivatives, linear algebra). I hope that a new version or a new book will go more in depth with the actual problems...
6 of 7 people found the following review helpful.
No-buy for a beginner and not useful for a professional
By Grace
The book is more like a quick compilation of a college student's note. Concepts are presented in a relatively isolated manner; connections between concepts are, for the large part, missing. Furthermore, if the materials presented are rather shallow like in this book, readers will expect to see a strong emphasis on, or hands on exercises of, practical applications. But this book doesn't seem to help much in that regard either, despite what the book claims. The book does give introduction to a bunch of models, which can be useful for a beginner. But at least this edition I wouldn't suggest any one to buy because of poor editing. For example, the pseudo code fragments listed in the book are often wrong, or different than what is described by the text. References to formulas, etc need some proof reading, too.
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