Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision: Techniques and Use Cases

Cover
Half Title
Title
Copyright
Dedication
Contents
About the Authors
Preface
Acknowledgments
Chapter 1 Introduction
	Learning Outcomes
	1.1 Introduction
		1.1.1 Subsets of Artificial Intelligence
		1.1.2 Three Horizons of Deep Learning Applications
		1.1.3 Natural Language Processing
		1.1.4 Speech Recognition
		1.1.5 Computer Vision
	1.2 Machine Learning Methods for NLP, Computer Vision (CV), and Speech
		1.2.1 Support Vector Machine (SVM)
		1.2.2 Bagging
		1.2.3 Gradient-boosted Decision Trees (GBDTs)
		1.2.4 Naïve Bayes
		1.2.5 Logistic Regression
		1.2.6 Dimensionality Reduction Techniques
	1.3 Tools, Libraries, Datasets, and Resources for the Practitioners
		1.3.1 TensorFlow
		1.3.2 Keras
		1.3.3 Deeplearning4j
		1.3.4 Caffe
		1.3.5 ONNX
		1.3.6 PyTorch
		1.3.7 scikit-learn
		1.3.8 NumPy
		1.3.9 Pandas
		1.3.10 NLTK
		1.3.11 Gensim
		1.3.12 Datasets
	1.4 Summary
	Bibliography
Chapter 2 Natural Language Processing
	Learning Outcomes
	2.1 Natural Language Processing
	2.2 Generic NLP Pipeline
		2.2.1 Data Acquisition
		2.2.2 Text Cleaning
	2.3 Text Pre-processing
		2.3.1 Noise Removal
		2.3.2 Stemming
		2.3.3 Tokenization
		2.3.4 Lemmatization
		2.3.5 Stop Word Removal
		2.3.6 Parts of Speech Tagging
	2.4 Feature Engineering
	2.5 Modeling
		2.5.1 Start with Simple Heuristics
		2.5.2 Building Your Model
		2.5.3 Metrics to Build Model
	2.6 Evaluation
	2.7 Deployment
	2.8 Monitoring and Model Updating
	2.9 Vector Representation for NLP
		2.9.1 One Hot Vector Encoding
		2.9.2 Word Embeddings
		2.9.3 Bag of Words
		2.9.4 TF-IDF
		2.9.5 N-gram
		2.9.6 Word2Vec
		2.9.7 Glove
		2.9.8 ElMo
	2.10 Language Modeling with n-grams
		2.10.1 Evaluating Language Models
		2.10.2 Smoothing
		2.10.3 Kneser-Ney Smoothing
	2.11 Vector Semantics and Embeddings
		2.11.1 Lexical Semantics
		2.11.2 Vector Semantics
		2.11.3 Cosine for Measuring Similarity
		2.11.4 Bias and Embeddings
	2.12 Summary
	Bibliography
Chapter 3 State-of-the-Art Natural Language Processing
	Learning Outcomes
	3.1 Introduction
	3.2 Sequence-to-Sequence Models
		3.2.1 Sequence
		3.2.2 Sequence Labeling
		3.2.3 Sequence Modeling
	3.3 Recurrent Neural Networks
		3.3.1 Unrolling RNN
		3.3.2 RNN-based POS Tagging Use Case
		3.3.3 Challenges in RNN
	3.4 Attention Mechanisms
		3.4.1 Self-attention Mechanism
		3.4.2. Multi-head Attention Mechanism
		3.4.3 Bahdanau Attention
		3.4.4 Luong Attention
		3.4.5 Global Attention versus Local Attention
		3.4.6 Hierarchical Attention
	3.5 Transformer Model
		3.5.1 Bidirectional Encoder, Representations, and Transformers (BERT)
		3.5.2 GPT3
	3.6 Summary
	Bibliography
Chapter 4 Applications of Natural Language Processing
	Learning Outcomes
	4.1 Introduction
	4.2 Word Sense Disambiguation
		4.2.1 Word Senses
		4.2.2 WordNet: A Database of Lexical Relations
		4.2.3 Approaches to Word Sense Disambiguation
		4.2.4 Applications of Word Sense Disambiguation
	4.3 Text Classification
		4.3.1 Building the Text Classification Model
		4.3.2 Applications of Text Classification
		4.3.3 Other Applications
	4.4 Sentiment Analysis
		4.4.1 Types of Sentiment Analysis
	4.5 Spam Email Classification
		4.5.1 History of Spam
		4.5.2 Spamming Techniques
		4.5.3 Types of Spams
	4.6 Question Answering
		4.6.1 Components of Question Answering System
		4.6.2 Information Retrieval-based Factoid Question and Answering
		4.6.3 Entity Linking
		4.6.4 Knowledge-based Question Answering
	4.7 Chatbots and Dialog Systems
		4.7.1 Properties of Human Conversation
		4.7.2 Chatbots
		4.7.3 The Dialog-state Architecture
	4.8 Summary
	Bibliography
Chapter 5 Fundamentals of Speech Recognition
	Learning Outcomes
	5.1 Introduction
		5.2 Structure of Speech
	5.3 Basic Audio Features
		5.3.1 Pitch
		5.3.2 Timbral Features
		5.3.3 Rhythmic Features
		5.3.4 MPEG-7 Features
	5.4 Characteristics of Speech Recognition System
		5.4.1 Pronunciations
		5.4.2 Vocabulary
		5.4.3 Grammars
		5.4.4 Speaker Dependence
	5.5 The Working of a Speech Recognition System
		5.5.1 Input Speech
		5.5.2 Audio Pre-processing
		5.5.3 Feature Extraction
	5.6 Audio Feature Extraction Techniques
		5.6.1 Spectrogram
		5.6.2 MFCC
		5.6.3 Short-Time Fourier Transform
		5.6.4 Linear Prediction Coefficients (LPCC)
		5.6.5 Discrete Wavelet Transform (DWT)
		5.6.6 Perceptual Linear Prediction (PLP)
	5.7 Statistical Speech Recognition
		5.7.1 Acoustic Model
		5.7.2 Pronunciation Model
		5.7.3 Language Model
		5.7.4 Conventional ASR Approaches
	5.8 Speech Recognition Applications
		5.8.1 In Banking
		5.8.2 In-Car Systems
		5.8.3 Health Care
		5.8.4 Experiments by Different Speech Groups for Large-Vocabulary Speech Recognition
		5.8.5 Measure of Performance
	5.9 Challenges in Speech Recognition
		5.9.1 Vocabulary Size
		5.9.2 Speaker-Dependent or -Independent
		5.9.3 Isolated, Discontinuous, and Continuous Speech
		5.9.4 Phonetics
		5.9.5 Adverse Conditions
	5.10 Open-source Toolkits for Speech Recognition
		5.10.1 Frameworks
		5.10.2 Additional Tools and Libraries
	5.11 Summary
	Bibliography
Chapter 6 Deep Learning Models for Speech Recognition
	Learning Outcomes
	6.1 Traditional Methods of Speech Recognition
		6.1.1 Hidden Markov Models (HMMs)
		6.1.2 Gaussian Mixture Models (GMMs)
		6.1.3 Artificial Neural Network (ANN)
		6.1.4 HMM and ANN Acoustic Modeling
		6.1.5 Deep Belief Neural Network (DBNN) for Acoustic Modelling
	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
	Bibliography
Chapter 7 End-to-End Speech Recognition Models
	Learning Outcomes
	7.1 End-to-End Speech Recognition Models
		7.1.1 Definition of End-to-End ASR System
		7.1.2 Connectionist Temporal Classification (CTC)
		7.1.3 Deep Speech
		7.1.4 Deep Speech 2
		7.1.5 Listen, Attend, Spell (LAS) Model
		7.1.6 JASPER
		7.1.7 QuartzNet
	7.2 Self-supervised Models for Automatic Speech Recognition
		7.2.1 Wav2Vec
		7.2.2 Data2Vec
		7.2.3 HuBERT
	7.3 Online/Streaming ASR
		7.3.1 RNN-transducer-Based Streaming ASR
		7.3.2 Wav2Letter for Streaming ASR
		7.3.3 Conformer Model
	7.4 Summary
	Bibliography
Chapter 8 Computer Vision Basics
	Learning Outcomes
	8.1 Introduction
		8.1.1 Fundamental Steps for Computer Vision
		8.1.2 Fundamental Steps in Digital Image Processing
	8.2 Image Segmentation
		8.2.1 Steps in Image Segmentation
	8.3 Feature Extraction
	8.4 Image Classification
		8.4.1 Image Classification Using Convolutional Neural Network (CNN)
		8.4.2 Convolution Layer
		8.4.3 Pooling or Down Sampling Layer
		8.4.4 Flattening Layer
		8.4.5 Fully Connected Layer
		8.4.6 Activation Function
	8.5 Tools and Libraries for Computer Vision
		8.5.1 OpenCV
		8.5.2 MATLAB
	8.6 Applications of Computer Vision
		8.6.1 Object Detection
		8.6.2 Face Recognition
		8.6.3 Number Plate Identification
		8.6.4 Image-based Search
		8.6.5 Medical Imaging
	8.7 Summary
	Bibliography
Chapter 9 Deep Learning Models for Computer Vision
	Learning Outcomes
	9.1 Deep Learning for Computer Vision
	9.2 Pre-trained Architectures for Computer Vision
		9.2.1 LeNet
		9.2.2 AlexNet
		9.2.3 VGG
		9.2.4 Inception
		9.2.5 R-CNN
		9.2.6 Fast R-CNN
		9.2.7 Faster R-CNN
		9.2.8 Mask R-CNN
		9.2.9 YOLO
	9.3 Summary
	Bibliography
Chapter 10 Applications of Computer Vision
	Learning Outcomes
	10.1 Introduction
	10.2 Optical Character Recognition
		10.2.1 Code Snippets
		10.2.2 Result Analysis
	10.3 Face and Facial Expression Recognition
		10.3.1 Face Recognition
		10.3.2 Facial Recognition System
		10.3.3 Major Challenges in Recognizing Face Expression
		10.3.4 Result Analysis
	10.4 Visual-based Gesture Recognition
		10.4.1 Framework Used
		10.4.2 Code Snippets
		10.4.3 Result Analysis
		10.4.4 Major Challenges in Gesture Recognition
	10.5 Posture Detection and Correction
		10.5.1 Framework Used
		10.5.2 Squats
		10.5.3 Result Analysis
	10.6 Summary
	Bibliography
Index