Tensorflow for Deep Learning: From Linear Regression to Reinforcement Learning
Ramsundar, Bharath/ Zadeh, Reza Bosagh
Language: English
Published by Oreilly & Associates Inc, 2018
- Softcover
- New
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
AbeBooks seller since January 6, 2003
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300 pages. 9.25x7.00x0.75 inches. In Stock. This item is printed on demand.
Seller Inventory # __1491980451
- Title
- Tensorflow for Deep Learning: From Linear Regression to Reinforcement Learning
- Author
- Ramsundar, Bharath/ Zadeh, Reza Bosagh
- Publisher
- Oreilly & Associates Inc
- Publication year
- 2018
- Condition
- Brand New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1491980451
- ISBN 13
- 9781491980453
- Item weight
- 0.51 kilograms
Learn how to solve challenging machine learning problems with TensorFlow, Googleâ s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines.
TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. Itâ s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms.
- Learn TensorFlow fundamentals, including how to perform basic computation
- Build simple learning systems to understand their mathematical foundations
- Dive into fully connected deep networks used in thousands of applications
- Turn prototypes into high-quality models with hyperparameter optimization
- Process images with convolutional neural networks
- Handle natural language datasets with recurrent neural networks
- Use reinforcement learning to solve games such as tic-tac-toe
- Train deep networks with hardware including GPUs and tensor processing units
"Synopsis" may belong to another edition of this title.
About the Author
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).
"About the title" may belong to another edition of this title.
Revaluation Books
Exeter, United Kingdom
AbeBooks seller since January 6, 2003
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