Deep Learning with Tensorflow

✍ Scribed by Zaccone, Giancarlo;Karim, Mohammad Rezaul;Menshawy, Ahmed

• Publisher: Packt Publishing
• Year: 2017
• Tongue: English
• Leaves: 316
• Category: Library

✦ Synopsis

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guideAbout This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Real-world contextualization through some deep learning problems concerning research and application Who This Book Is ForThe book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.What You Will Learn Learn about machine learning landscapes along with the historical development and progress of deep learning Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x Access public datasets and utilize them using TensorFlow to load, process, and transform data Use TensorFlow on real-world datasets, including images, text, and more Learn how to evaluate the performance of your deep learning models Using deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications In DetailDeep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x.Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.Style and approachThis step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.

✦ Table of Contents

Cover......Page 1
Copyright......Page 3
Credits......Page 4
About the Authors......Page 5
About the Reviewers......Page 7
www.PacktPub.com......Page 9
Customer Feedback......Page 10
Table of Contents......Page 11
Preface......Page 17
Chapter 1: Getting Started with Deep Learning......Page 23
Supervised learning......Page 24
Reinforcement learning......Page 25
How the human brain works......Page 26
Deep learning history......Page 27
The biological neuron......Page 28
An artificial neuron......Page 29
The backpropagation algorithm......Page 31
Weights optimization......Page 32
Stochastic gradient descent......Page 33
Multilayer perceptron......Page 35
Convolutional Neural Networks......Page 36
Restricted Boltzmann Machines......Page 37
Autoencoders......Page 38
Recurrent Neural Networks......Page 39
Deep learning framework comparisons......Page 40
Summary......Page 44
Chapter 2: First Look at TensorFlow......Page 45
What's new with TensorFlow 1.x?......Page 46
How does it change the way people use it?......Page 47
Installing TensorFlow on Linux......Page 48
Requirements for running TensorFlow with GPU from NVIDIA......Page 49
Step 2: Installing NVIDIA cuDNN v5.1+......Page 50
Step 5: Installing Python (or Python3)......Page 52
Step 6: Installing and upgrading PIP (or PIP3)......Page 53
Installing TensorFlow with native pip......Page 54
Installing with virtualenv......Page 55
Installation from source......Page 57
Computational graphs......Page 59
Why a computational graph?......Page 60
Neural networks as computational graphs......Page 61
The programming model......Page 63
Data model......Page 65
Shape......Page 66
Data types......Page 67
Variables......Page 70
Feeds......Page 71
How does TensorBoard work?......Page 72
Implementing a single input neuron......Page 73
Migrating to TensorFlow 1.x......Page 80
How to upgrade using the script......Page 81
Summary functions......Page 85
Simplified mathematical variants......Page 86
Summary......Page 87
Chapter 3: Using TensorFlow on a Feed-Forward Neural Network......Page 89
Introducing feed-forward neural networks......Page 90
Feed-forward and backpropagation......Page 91
Transfer functions......Page 92
Classification of handwritten digits......Page 94
Exploring the MNIST dataset......Page 95
Softmax classifier......Page 97
Visualization......Page 103
Restoring a model......Page 105
Softmax source code......Page 108
Softmax loader source code......Page 109
Implementing a five-layer neural network......Page 110
Visualization......Page 113
Five-layer neural network source code......Page 115
ReLU classifier......Page 116
Visualization......Page 118
Source code for the ReLU classifier......Page 119
Dropout optimization......Page 121
Visualization......Page 124
Source code for dropout optimization......Page 125
Summary......Page 128
Chapter 4: TensorFlow on a Convolutional Neural Network......Page 129
Introducing CNNs......Page 130
CNN architecture......Page 132
A model for CNNs - LeNet......Page 134
Building your first CNN......Page 135
Source code for a handwritten classifier......Page 144
Emotion recognition with CNNs......Page 146
Source code for emotion classifier......Page 157
Testing the model on your own image......Page 161
Source code......Page 164
Summary......Page 166
Chapter 5: Optimizing TensorFlow Autoencoders......Page 167
Introducing autoencoders......Page 168
Implementing an autoencoder......Page 169
Source code for the autoencoder......Page 175
Improving autoencoder robustness......Page 176
Building a denoising autoencoder......Page 177
Source code for the denoising autoencoder......Page 184
Convolutional autoencoders......Page 186
Decoder......Page 187
Source code for convolutional autoencoder......Page 197
Summary......Page 200
RNNs basic concepts......Page 201
Unfolding an RNN......Page 203
The vanishing gradient problem......Page 204
LSTM networks......Page 205
An image classifier with RNNs......Page 206
Source code for RNN image classifier......Page 212
Bidirectional RNNs......Page 214
Source code for the bidirectional RNN......Page 219
Text prediction......Page 221
PTB model......Page 222
Running the example......Page 223
Summary......Page 225
Chapter 7: GPU Computing......Page 226
GPGPU history......Page 227
The CUDA architecture......Page 228
GPU programming model......Page 229
TensorFlow GPU set up......Page 230
TensorFlow GPU management......Page 233
Programming example......Page 234
Source code for GPU computation......Page 235
Assigning a single GPU on a multi-GPU system......Page 237
Source code for GPU with soft placement......Page 239
Source code for multiple GPUs management......Page 240
Summary......Page 241
Chapter 8: Advanced TensorFlow Programming......Page 242
Introducing Keras......Page 243
Installation......Page 244
Building deep learning models......Page 245
Sentiment classification of movie reviews......Page 247
Source code for the Keras movie classifier......Page 250
Adding a convolutional layer......Page 251
Source code for movie classifier with convolutional layer......Page 252
Pretty Tensor......Page 253
Sequential mode......Page 254
Digit classifier......Page 255
Source code for digit classifier......Page 258
Titanic survival predictor......Page 261
Source code for titanic classifier......Page 265
Summary......Page 266
Introduction to multimedia analysis......Page 267
Deep learning for Scalable Object Detection......Page 268
Using the retrained model......Page 270
Accelerated Linear Algebra......Page 272
Just-in-time compilation via XLA......Page 273
JIT compilation......Page 276
Existence and advantages of XLA......Page 277
Under the hood working of XLA......Page 278
TensorFlow and Keras......Page 279
Effects of having Keras on board......Page 280
Video question answering system......Page 281
Deep learning on Android......Page 288
TensorFlow demo examples......Page 289
Building with Android studio......Page 292
Going deeper - Building with Bazel......Page 293
Summary......Page 295
Chapter 10: Reinforcement Learning......Page 296
Basic concepts of Reinforcement Learning......Page 297
Q-learning algorithm......Page 299
Introducing the OpenAI Gym framework......Page 301
FrozenLake-v0 implementation problem......Page 302
Source code for the FrozenLake-v0 problem......Page 305
Q-learning with TensorFlow......Page 306
Source code for the Q-learning neural network......Page 309
Summary......Page 311
Index......Page 312

✦ Subjects

Computer Science;Programming