Programming with TensorFlow: Solution for Edge Computing Applications (EAI/Springer Innovations in Communication and Computing)

Preface
Acknowledgement
Contents
Introduction to Tensorflow Package
	1 Why Tensorflow for Deep Learning?
	2 Installation Guide to Tensorflow
		2.1 System Requirement
	References
Tensorflow Basics
	1 Hello Tensorflow Program
	2 Representation of Vector/Matrix
	3 With Session() Vs without Session()
	4 Zeros Matrix and Ones Matrix
	5 Make Matrix Negative
	6 Variables and Constants
	7 Variables Concept in Tensorflow
	8 Implement Concept of Placeholder
	9 Simple Equations Using Tensorflow
	10 Simple Operations in Tensorflow
	References
Visualizations
	1 Matplotlib in Tensorflow
		1.1 Histogram Implementation
		1.2 Trigonometric Curves
	References
Regression
	1 Regression Model – Simple Linear Equation
	2 Linear Regression
	3 Logistic Regression
	4 Linear Regression by Importing Datasets
	5 Logistic Regression by Importing Dataset
	References
Neural Network
	1 Inside the Code
	References
Convolutional Neural Network
	1 How Does It Work?
	References
Recurrent Neural Network
	1 How They Work?
		1.1 Backpropagation Through Time (BPTT)
		1.2 In the Code
	2 Long Short-Term Memory
		2.1 LSTM In Keras
	References
Application of Machine Learning and Deep Learning
	1 Automobile Industry
	2 Climate Change
	3 Disaster Management
	4 Education
	5 Energy
	6 Entertainment and Media
		6.1 AlphaGo
		6.2 Voice Generation
		6.3 Music Generation
		6.4 Restoring Sounds in Video
		6.5 Automatically Writing Wikipedia
		6.6 Deep-Fake Detection
		6.7 Multi-Agent Systems
		6.8 Image Synthesis
		6.9 Graphic Generator
	7 Finance
	References
Chatbot
	References
PyTorch
	1 The Significant Highlights of PyTorch
	2 Why We Prefer PyTorch
	3 Requirements for Implementing Deep Learning
	4 PyTorch Basic Components
		4.1 Tensor
		4.2 Autograd Module
	5 Implement the Neural Network Using PyTorch
	6 Difference Between PyTorch and Tensorflow
	7 PyTorch for Computer Vision
		7.1 Image Classifier
		7.2 Image Augmentation in Less Data
	8 Sequential Data Models
		8.1 LSTM in PyTorch
	9 Summary
	References
Pattern Recognition and Machine Learning
	1 Kernel Support Vector Machine
		1.1 Linear Kernel
		1.2 RBF Kernel
		1.3 Polynomial Kernel
	2 Kernel Ridge Regression
	3 Kernel Density Estimator
		3.1 Density Estimation
		3.2 Constructing a Kernel Density Estimate
		3.3 Features of the Algorithm
			3.3.1 Bandwidth Selection
			3.3.2 Kernels
			3.3.3 Heterogeneous Data
			3.3.4 Fast Fourier Transform–Based Computations
			3.3.5 Tree-Based Computations
			3.3.6 Computational Efficiency
	4 Dimensionality Reduction with Kernel Principal Component Analysis
	5 Hidden Markov Model to Estimate the Behavior of a Person or Animal
	6 Factor Analysis
	7 Twitter Sentiment Analysis
	References
Programming Tensor Flow with Single Board Computers
	1 Introduction
	2 CUDA Programming in NVIDIA
	3 Prepackaging Inspection Module for Industry 4.0
	4 Fish Geopositioning System for Industry 4.0
	5 Conclusion
	References
Appendices
	Appendix 1
	Appendix 2
References
Data Set Web References
References Books
Article References
Index