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

Description

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 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