Titel: TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
Verlag: O'Reilly Media
Erscheinungsjahr: 2018
Sprache: Englisch
ISBN-10: 1491980451
ISBN-13: 9781491980453
Einband: Softcover
Zustand: New

�ber diesen Titel

Inhaltsangabe

Learn how to solve challenging machine learning problems with TensorFlow, Google� 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. It� 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

�ber die Autorin bzw. den Autor

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.

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