1st Edition
Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision Techniques and Use Cases
1 Introduction 1.1 Introduction; 1.2 Machine Learning Methods for NLP, Computer Vision (CV), and Speech; 1.3 Tools, Libraries, Datasets, and Resources for the Practitioners; 1.4 Summary 2 Natural Language Processing 2.1 Natural Language Processing; 2.2 Generic NLP Pipeline; 2.3 Text Pre-processing; 2.4 Feature Engineering; 2.5 Modeling; 2.6 Evaluation; 2.7 Deployment; 2.8 Monitoring and Model Updating; 2.9 Vector Representation for NLP; 2.10 Language Modeling with n-grams; 2.11 Vector Semantics and Embeddings; 2.12 Summary 3 State-of-the-Art Natural Language 3.1 Introduction; 3.2 Sequence-to-Sequence Models; 3.3 Recurrent Neural Networks; 3.4 Attention Mechanisms; 3.5 Transformer Model; 3.6 Summary 4 Applications of Natural Language Processing 4.1 Introduction; 4.2 Word Sense Disambiguation; 4.3 Text Classification; 4.4 Sentiment Analysis; 4.5 Spam Email Classification; 4.6 Question Answering; 4.7 Chatbots and Dialog Systems; 4.8 Summary 5 Fundamentals of Speech Recognition 5.1 Introduction; 5.2 Structure of Speech; 5.3 Basic Audio Features; 5.4 Characteristics of Speech Recognition System; 5.5 The Working of a Speech Recognition System; 5.6 Audio Feature Extraction Techniques; 5.7 Statistical Speech Recognition; 5.8 Speech Recognition Applications; 5.9 Challenges in Speech Recognition; 5.10 Open-source Toolkits for Speech Recognition; 5.11 Summary 6 Deep Learning Models for Speech Recognition 6.1 Traditional Methods of Speech Recognition; 6.2 RNN-based Encoder–Decoder Architecture; 6.3 Encoder; 6.4 Decoder; 6.5 Attention-based Encoder–Decoder Architecture; 6.6 Challenges in Traditional ASR and the Motivation for End-to-End ASR; 6.7 Summary 7 End-to-End Speech Recognition Models 7.1 End-to-End Speech Recognition Models; 7.2 Self-supervised Models for Automatic Speech Recognition; 7.3 Online/Streaming ASR; 7.4 Summary 8 Computer Vision Basics 8.1 Introduction; 8.2 Image Segmentation; 8.3 Feature Extraction; 8.4 Image Classification; 8.5 Tools and Libraries for Computer Vision; 8.6 Applications of Computer Vision; 8.7 Summary 9 Deep Learning Models for Computer Vision 9.1 Deep Learning for Computer Vision; 9.2 Pre-trained Architectures for Computer Vision; 9.3 Summary 10 Applications of Computer Vision 10.1 Introduction; 10.2 Optical Character Recognition; 10.3 Face and Facial Expression Recognition; 10.4 Visual-based Gesture Recognition; 10.5 Posture Detection and Correction; 10.6 Summary
Biography
L. Ashok Kumar, D. Karthika Renuka
