Deep Learning with Applications Using Python Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras 1st Edition by Navin Kumar Manaswi ISBN 1484235169 9781484235164

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ISBN 10: 1484235169 
ISBN 13: 9781484235164
Author: Navin Kumar Manaswi

Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn.  What You Will Learn  Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Use face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Engage with chatbots using deep learning Who This Book Is For Data scientists and developers who want to adapt and build deep learning applications.

Deep Learning with Applications Using Python Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras 1st Table of contents:

Chapter 1: Introduction to Deep Learning

1. What is Deep Learning?
2. Machine Learning vs. Deep Learning
3. Key Concepts and Terminology
4. Overview of Neural Networks
5. Introduction to TensorFlow and Keras
6. Applications of Deep Learning in Real-World Problems
7. Setup and Installation of Python Libraries

Chapter 2: Basics of Neural Networks

1. Understanding Artificial Neural Networks (ANNs)
2. Structure of Neural Networks: Neurons, Layers, and Activation Functions
3. Feedforward and Backpropagation
4. Training Neural Networks: Gradient Descent and Optimization
5. Introduction to Keras API
6. Building Your First Neural Network Model in Keras

Chapter 3: Chatbot Development with Deep Learning

1. Introduction to Chatbots and Natural Language Processing (NLP)
2. Types of Chatbots: Rule-Based vs. AI-Based
3. Collecting and Preparing Text Data for Chatbots
4. Understanding Sequence Models and RNNs (Recurrent Neural Networks)
5. Building a Simple Chatbot with Keras and TensorFlow
6. Training, Testing, and Evaluating the Chatbot Model
7. Advanced Techniques: Adding Context and Memory to the Chatbot

Chapter 4: Face Recognition with Deep Learning

1. Introduction to Face Recognition and Its Applications
2. Preparing Face Data for Training
3. Convolutional Neural Networks (CNNs) for Image Classification
4. Building a Face Recognition System Using CNNs and Keras
5. Face Detection and Preprocessing
6. Implementing Transfer Learning with Pretrained Models
7. Evaluating the Face Recognition Model

Chapter 5: Object Recognition with Deep Learning

1. Introduction to Object Detection and Recognition
2. Object Detection vs. Object Classification
3. Preparing Image Datasets for Object Detection
4. Convolutional Neural Networks (CNNs) in Object Recognition
5. Building an Object Recognition System with Keras
6. Fine-Tuning Pretrained Models for Object Detection
7. Real-Time Object Detection with TensorFlow

Chapter 6: Speech Recognition with Deep Learning

1. Introduction to Speech Recognition and Its Applications
2. Signal Processing Techniques for Audio Data
3. Preparing Audio Data for Speech Recognition
4. Building a Speech Recognition Model Using Deep Learning
5. Using Recurrent Neural Networks (RNNs) and LSTMs for Speech Recognition
6. Speech-to-Text Conversion with TensorFlow and Keras
7. Evaluating and Tuning the Speech Recognition Model

Chapter 7: Advanced Deep Learning Techniques

1. Introduction to Transfer Learning
2. Using Pretrained Models for New Applications
3. Fine-Tuning Neural Networks for Specific Tasks
4. Techniques for Improving Model Performance
5. Hyperparameter Tuning and Optimization
6. Generative Models: GANs (Generative Adversarial Networks)
7. Reinforcement Learning: An Introduction

Chapter 8: Deploying Deep Learning Models

1. Introduction to Model Deployment
2. Exporting and Saving Models with TensorFlow and Keras
3. Deploying Models for Real-Time Applications
4. Building a Web Application to Deploy a Chatbot
5. Deploying Object and Face Recognition Systems
6. Implementing Speech Recognition in Mobile Applications
7. Using Cloud Platforms for Model Deployment

Chapter 9: Ethical Considerations and Future Directions

1. Ethical Issues in AI and Deep Learning
2. Bias and Fairness in Deep Learning Models
3. The Impact of Deep Learning on Privacy and Security
4. Responsible AI Development and Deployment
5. Future Trends in Deep Learning Applications
6. Challenges and Opportunities in the Field of AI

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