the APPLIED TENSORFLOW AND KERAS WORKSHOP - build your practical skills by working through a real... -world project - a bitcoin price prediction tracke.

173 Pages·2020·9.944 MB·English

by  HARVEEN SINGH CAPELO LUIS CHADHA

The
Applied
TensorFlow
and Keras
Workshop
Develop your practical skills by working through a
real-world project and build your own Bitcoin price
prediction tracker
Harveen Singh Chadha and Luis Capelo
The Applied TensorFlow and Keras Workshop
Copyright © 2020 Packt Publishing
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Authors: Harveen Singh Chadha and Luis Capelo
Reviewers: Abhranshu Bagchi, Achint Chaudhary, Vishal Chauhan, Alexis Rutherford,
and Subhash Sundaravadivelu
Managing Editor: Anushree Arun Tendulkar
Acquisitions Editors: Sneha Shinde and Karan Wadekar
Production Editor: Roshan Kawale
Editorial Board: Megan Carlisle, Samuel Christa, Mahesh Dhyani, Heather Gopsill,
Manasa Kumar, Alex Mazonowicz, Monesh Mirpuri, Bridget Neale, Dominic Pereira,
Shiny Poojary, Abhishek Rane, Brendan Rodrigues, Erol Staveley, Ankita Thakur,
Nitesh Thakur, and Jonathan Wray
First published: July 2020
Production reference: 1290720
ISBN: 978-1-80020-121-7
Published by Packt Publishing Ltd.
Livery Place, 35 Livery Street
Birmingham B3 2PB, UK
Table of Contents
Preface i
Chapter 1: Introduction to Neural Networks
and Deep Learning 1
Introduction .............................................................................................. 2
What are Neural Networks? ................................................................... 2
Successful Applications of Neural Networks ............................................ 3
Why Do Neural Networks Work So Well? .............................................. 6
Representation Learning ............................................................................ 6
Function Approximation ............................................................................. 7
Limitations of Deep Learning ..................................................................... 8
Inherent Bias and Ethical Considerations ................................................. 9
Common Components and Operations of Neural Networks ............... 10
Configuring a Deep Learning Environment ....................................... 12
Software Components for Deep Learning .............................................. 12
Python 3 ...............................................................................................................13
TensorFlow ..........................................................................................................13
Keras .....................................................................................................................13
TensorBoard ........................................................................................................14
Jupyter Notebook, Pandas, and NumPy ..........................................................14
Exercise 1.01: Verifying the Software Components ............................... 16
Exploring a Trained Neural Network .................................................. 22
The MNIST Dataset .................................................................................... 23
Training a Neural Network with TensorFlow ......................................... 25
Exercise 1.02: Training a Neural Network Using the MNIST Dataset .. 26
Testing Network Performance with Unseen Data ................................. 28
Activity 1.01: Training a Neural Network with Different
Hyperparameters ....................................................................................... 30
Summary ................................................................................................ 31
Chapter 2: Real-World Deep Learning:
Predicting the Price of Bitcoin 33
Introduction ........................................................................................... 34
Choosing the Right Model Architecture ............................................. 34
Convolutional Neural Networks (CNNs) ................................................. 35
Recurrent Neural Networks (RNNs) ........................................................ 36
Long Short-Term Memory (LSTM) Networks .......................................... 37
Generative Adversarial Networks ............................................................ 38
Deep Reinforcement Learning (DRL) ....................................................... 40
Data Normalization ................................................................................... 41
Z-Score .................................................................................................................42
Point-Relative Normalization ............................................................................42
Maximum and Minimum Normalization .........................................................43
Structuring Your Problem .................................................................... 44
Jupyter Notebook ....................................................................................... 46
Exercise 2.01: Exploring the Bitcoin Dataset and Preparing
Data for a Model ........................................................................................ 46
Using Keras as a TensorFlow Interface .............................................. 56
Model Components ................................................................................... 56
Exercise 2.02: Creating a TensorFlow Model Using Keras ..................... 59
From Data Preparation to Modeling .................................................. 61
Training a Neural Network ....................................................................... 62
Reshaping Time Series Data ..................................................................... 64
Making Predictions .................................................................................... 67
Overfitting ...........................................................................................................67
Activity 2.01: Assembling a Deep Learning System ............................... 68
Summary ................................................................................................ 69
Chapter 3: Real-World Deep Learning:
Evaluating the Bitcoin Model 71
Introduction ........................................................................................... 72
Problem Categories ................................................................................... 72
Loss Functions, Accuracy, and Error Rates ............................................. 73
Different Loss Functions, Same Architecture ......................................... 75
Using TensorBoard .................................................................................... 78
Implementing Model Evaluation Metrics ................................................ 79
Evaluating the Bitcoin Model .................................................................... 81
Overfitting ................................................................................................... 83
Model Predictions ...................................................................................... 83
Interpreting Predictions ............................................................................ 85
Exercise 3.01: Creating an Active Training Environment ...................... 87
Hyperparameter Optimization ........................................................... 93
Layers and Nodes – Adding More Layers ................................................ 93
Adding More Nodes ............................................................................................94
Layers and Nodes – Implementation ...................................................... 94
Epochs ......................................................................................................... 96
Epochs – Implementation ..................................................................................96
Activation Functions .................................................................................. 97
Linear (Identity) Functions ........................................................................ 97
Hyperbolic Tangent (Tanh) Function ................................................................98
Rectified Linear Unit Functions .........................................................................99
Activation Functions – Implementation ................................................ 100
Regularization Strategies ........................................................................ 101
L2 Regularization ..............................................................................................101
Dropout ..............................................................................................................102
Regularization Strategies – Implementation .................................................102
Optimization Results ............................................................................... 103
Activity 3.01: Optimizing a Deep Learning Model ................................ 104
Summary .............................................................................................. 105
Chapter 4: Productization 107
Introduction ......................................................................................... 108
Handling New Data ............................................................................. 108
Separating Data and Model .................................................................... 108
The Data Component .............................................................................. 109
The Model Component ............................................................................ 109
Dealing with New Data ............................................................................ 110
Retraining an Old Model ..................................................................................111
Training a New Model ......................................................................................112
Exercise 4.01: Retraining a Model Dynamically .................................... 113
Deploying a Model as a Web Application .............................................. 119
Application Architecture and Technologies ......................................... 120
Exercise 4.02: Deploying and Using Cryptonic ..................................... 121
Activity 4.01: Deploying a Deep Learning Application ......................... 125
Summary .............................................................................................. 127
Appendix 129
Index 153

Preface