Beginning with Deep Learning Using TensorFlow: A Beginners Guide to TensorFlow and Keras for Practicing Deep Learning Principles and Applications (English Edition)

✍ Scribed by Mohan Kumar Silaparasetty

• Publisher: BPB Publications
• Year: 2022
• Tongue: English
• Leaves: 315
• Category: Library

✦ Synopsis

A Practicing Guide to TensorFlow and Deep Learning

Key Features

● Equipped with a necessary introduction to Deep Learning and AI.

● Includes demos and templates to give your projects a good start.

● Find more on the most important facets of AI, image, and speech recognition.

Description

This book begins with the configuration of an Anaconda development environment, essential for practicing the deep learning process. The basics of machine learning, which are needed for Deep Learning, are explained in this book.

TensorFlow is the industry-standard library for Deep Learning, and thereby, it is covered extensively with both versions, 1.x and 2.x. As neural networks are the heart of Deep Learning, the book explains them in great detail and systematic fashion, beginning with a single neuron and progressing through deep multilayer neural networks. The emphasis of this book is on the practical application of key concepts rather than going in-depth with them.

After establishing a firm basis in TensorFlow and Neural Networks, the book explains the concepts of image recognition using Convolutional Neural Networks (CNN), followed by speech recognition, and natural language processing (NLP). Additionally, this book discusses Transformers, the most recent advancement in NLP.

What you will learn

● Create machine learning models for classification and regression.

● Utilize TensorFlow 1.x to implement neural networks.

● Work with the Keras API and TensorFlow 2.

● Learn to design and train image categorization models.

● Construct translation and Q & A apps using transformer-based language models.

Who this book is for

This book is intended for those new to Deep Learning who want to learn about its principles and applications. Before you begin, you'll need to be familiar with Python.

Table of Contents

1.Introduction to Artificial Intelligence

2. Machine Learning

3. TensorFlow Programming

4. Neural Networks

5. TensorFlow 2

6. Image Recognition

7. Speech Recognition

✦ Table of Contents

Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewer
Acknowledgement
Preface
Errata
Table of Contents
1. Introduction to Artificial Intelligence
Structure
Objective
Brief history of artificial intelligence
Classification of AI
How did we reach here?
AI adoption by industries
Conclusion
Points to remember
2. Machine Learning
Introduction
Structure
Objectives
Defining machine learning
Supervised learning
Setting up the environment
Using Google Colab
Setting up local environment in Python
Prerequisite
Regression algorithms
Code demo
Multilinear regression
Logistic regression
Decision tree
Support vector machine (SVM)
Unsupervised learning
Conclusion
Questions
3. TensorFlow Programming
Introduction
Structure
Objective
TensorFlow development environment
Introducing TensorFlow
Elements of TensorFlow program
Constant
Variable
Placeholder
Session
Constants, variables, and placeholders
Linear algebra with TensorFlow
Optimizer
Applying optimizer to solve simple mathematical problems
Conclusion
Questions
4. Neural Networks
Introduction
Structure
Objective
About Neural Networks
Inputs
Weights
Bias
Net input function (F)
Activation function (G)
MNIST
MNIST—single layer multi-neuron model
Multilayer Neural Network
Multilayer binary classifier
ReLu activation function
Multilayer multiclass neural network
Conclusion
Questions
5. TensorFlow 2
Introduction
Structure
Objective
Installing TensorFlow 2
Using Anaconda Navigator
From Anaconda command prompt
Google Colab
What is new in TensorFlow 2?
Kera API
Classification with Iris data set
Conclusion
Points to remember
6. Image Recognition
Introduction
Structure
Objective
Introducing Convolutional Neural Networks (CNN)
Convolution layer
MNIST with CNN
Binary image classification with Keras
Multiclass image classification
Load from data frame—binary
Load from data frame—multiclass
Save and restore models
Pre-trained models
Transfer learning
Inference with Webcam images
Object detection
Conclusion
Points to remember
7. Speech Recognition
Introduction
Structure
Objective
What is speech recognition?—Historical perspective
Application of speech recognition
Natural Language Processing (NLP)
Word Embedding
Language model
Recurrent Neural Networks (RNN)
Text classification
Transformers
Pre-trained transformer models
BERT
Machine language translation
Q&A—SQUAD
Conclusion
Further reading
Index