Download Ebook TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

Download Ebook TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

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TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

Download Ebook TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

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About the Author

Bharath Ramsundar received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He is currently a PhD student in computer science at Stanford University with the Pande group. His research focuses on the application of deep-learning to drug-discovery. In particular, Bharath is the lead-developer and creator of DeepChem.io, an open source package founded on TensorFlow that aims to democratize the use of deep-learning in drug-discovery. He is supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences.Reza Bosagh Zadeh is Founder CEO at Matroid and Adjunct Professor at Stanford University. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. Reza received his PhD in Computational Mathematics from Stanford University under the supervision of Gunnar Carlsson. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award. He has served on the Technical Advisory Boards of Microsoft and Databricks. As part of his research, Reza built the Machine Learning Algorithms behind Twitter's who-to-follow system, the first product to use Machine Learning at Twitter. Reza is the initial creator of the Linear Algebra Package in Apache Spark and his work has been incorporated into industrial and academic cluster computing environments. In addition to research, Reza designed and teaches two PhD-level classes at Stanford: Distributed Algorithms and Optimization (CME 323), and Discrete Mathematics and Algorithms (CME 305).

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Product details

Paperback: 256 pages

Publisher: O'Reilly Media; 1 edition (March 23, 2018)

Language: English

ISBN-10: 1491980451

ISBN-13: 978-1491980453

Product Dimensions:

6.9 x 0.5 x 9.1 inches

Shipping Weight: 14.2 ounces (View shipping rates and policies)

Average Customer Review:

3.6 out of 5 stars

18 customer reviews

Amazon Best Sellers Rank:

#60,797 in Books (See Top 100 in Books)

This book has one page for every Data Scientific topic, each of which could take a book of its own. It is too short even for a review, not speaking about a textbook. Absolutely useless.

I am happy to have my book. The content is clear and rich. However on the delivery of my new book, some of the pages were crinkled.

Good fundamentals to understand how to code and play with tensors and python for Deep Learning

Had high expectations but the book totally ruined them. The book does not covers concepts which you might already know. Finally I had no idea whether this book is intended to teach more of tensorflow concepts or deep learning paradigms. In my opinion it failed to do both. The book starts of well explaining the core concepts of Tensorflow. But as you go into individual chapters for sequential processing or vision, they just shared the code and did a very poor job in explaining the Tensorflow Api. It is equivalent to seeing some code on github and try learning yourself using google.Since I already understand the core concepts like sessions/graphs this book is of no use to me. The worst part is that the code samples are the most basic you could get. For text processing they took Tensorflow.org tutorial and diluted it so much there is hardly anything to learn on text processing side.Essentially this book = basic concepts (which most people already know) + aggregation of github codes for each subject ( which are too basic and you can easily find much much better repositories online).The worst part is even the code samples are buggy. Even the basic linear regression code is wrong and does not optimise unless you change that. In my opinion the text processing code is wrong too, but I'm not too sure of it.

As a practicing software engineer interested in building my ML skills, I thought this was an excellent overview of modern machine learning and introduction to TensorFlow. The authors struck a nice balance between building your intuition for the theory behind different machine learning techniques and guiding you through sample TensorFlow code that implemented them. I appreciated that the chapters were motivated with real world examples, and I liked that some of the examples (e.g. DeepChem) were outside of the canonical machine learning problems you hear about in every other machine learning book / tutorial.In terms of the TensorFlow material, the book essentially starts from scratch by introducing TF primitives, and then walks you through increasingly complex applications from simple regressions to reinforcement learning. The code samples are digestible and well explained, and the accompanying GitHub repo is really helpful for taking a deep dive into the material. Throughout, the authors give helpful tips and tricks for practicing deep learning in the wild. At points I wish the book had gone slightly more in depth (with some of the more complicated material, as well as for things like preprocessing), but I liked that so much material was condensed into a relatively quick read.Highly recommended to anyone looking to level up with TensorFlow.

Overall, this is pretty okay. It's a decent introduction, although it could benefit from a deeper dive and more detail in the "hands-on" stuff.Compared to Learning TensorFlow by Hope, Resheff & Leider, I felt that this had a bit more depth and a bit more clarity (although I would have liked a bit more still).Like others, I found this to be a bit too focused on statistical chemistry, but it didn't really impede the value of the book, and I was able to learn some new things from it.

TensorFlow for Deep Learning by Ramsundar and Zadeh is 230 pages of great machine learning content that should compliment any data science library. If I had to complain, my largest gripe would be the strong bias toward the mathematical details of tensor calculus. Not that math is undesirable, but with only 230 pages to spare I felt that equations were often thrown out without adequate explanation.The introduction also comes on a little strong. The first chapter is named “Machine Learning Eats Computer Science”. Perhaps a better title would be “Deep Learning Hype at Full Throttle”. But let’s be real, deep learning is a subset of computer science – very useful for certain tasks and useless for others. The text would have you believe that deep learning is some new alien technology that is not related to algorithmic approaches at all.But this book has it where it counts. The structure of the chapters is laid out in a very intuitive manner that demonstrates that these authors know exactly what they are talking about and are eager to share the knowledge. First, Tensorflow primitive are introduced, next linear regression is explored, then on to fully connected deep networks. The fun really begins next with hyperparameter optimization, convolutional neural networks, recurrent neural networks, reinforced learning, and finally training. Relevant topics, logistically ordered, and adequately explained.It’s not a perfect book, however. Some of the diagrams and graphs have descriptions that refer to colors, yet all the images printed in the book are black and white. This makes some figures very difficult to interpret.The ending chapter on ethics also shares a lot in common with the hyped-up introduction – for example, dramatic fretting over sentient war terminators and suggesting quitting your job over questionable learning applications is a little much. In truth, governments leveraging technology to suppress freedom should be our concern – and this has been true for all time and all technologies. Enforceable checks and balances of a structured government have always been the best defense, not quitting a job… but I digress.Overall a very worthy addition to a data science library. You’ll probably want to have at least an introductory grasp on the Tensorflow and deep learning before reading this book, but it’s a great next step. Highly recommended.

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TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning PDF

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning PDF

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning PDF

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning PDF