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TensorFlow for Deep Learning(Early Release) 无水印pdf
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1. 1. Introduction to Deep Learning
1. Machine Learning eats Computer Science
2. Deep Learning Primitives
1. Fully Connected Layer
2. Convolutional Layer
3. Recurrent Neural Network (RNN) Layers
4. Long Short-Term Memory (LSTM) Cells
3. Deep Learning Zoo
1. LeNet
2. AlexNet
3. ResNet
4. Neural Captioning Model
5. Google Neural Machine Translation
6. One shot models
7. AlphaGo
8. Generative Adversarial Networks
9. Neural Turing Machines
4. Deep Learning Frameworks
5. Empirical Learning
2. 2. Introduction to Tensorflow Primitives
1. Introducing Tensors
1. Scalars, Vectors, and Matrices
2. Matrix Mathematics
3. Tensors
4. Tensors in physics.
5. Mathematical Asides
2. Basic Computations in Tensorflow
1. Initializing Constant Tensors.
2. Sampling Random Tensors
3. Tensor Addition and Scaling
4. Matrix Operations
5. Tensor Shape Manipulations
6. Introduction to Broadcasting
7. Tensorflow Graphs
8. Tensorflow Variables
3. Review
TensorFlow for Deep Learning
From Linear Regression to Reinforcement Learning
Reza Bosagh Zadeh, Bharath Ramsundar
TensorFlow for Deep Learning
by Reza Bosagh Zadeh and Bharath Ramsundar
Copyright © 2017 Reza Zadeh, Bharath Ramsundar. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc. , 1005 Gravenstein Highway North,
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Editor: Tim McGovern
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Interior Designer: David Futato
Cover Designer: Karen Montgomery
Illustrator: Rebecca Demarest
February 2018: First Edition
Revision History for the First
Edition
2017-03-29 First Early Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491980453 for release
details.
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc.
TensorFlow for Deep Learning, the cover image, and related trade dress are
trademarks of O’Reilly Media, Inc.
While the publisher and the author(s) have used good faith efforts to ensure that
the information and instructions contained in this work are accurate, the
publisher and the author(s) disclaim all responsibility for errors or omissions,
including without limitation responsibility for damages resulting from the use
of or reliance on this work. Use of the information and instructions contained
in this work is at your own risk. If any code samples or other technology this
work contains or describes is subject to open source licenses or the
intellectual property rights of others, it is your responsibility to ensure that
your use thereof complies with such licenses and/or rights.
978-1-491-98045-3
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