Applied Deep Learning: Tools, Techniques, and Implementation (Computational Intelligence Methods and Applications)

Preface
Acknowledgements
Contents
List of Figures
List of Tables
Part I: Introduction and Overview
	Chapter 1: Introduction
		1.1 Artificial Intelligence, Machine Learning, Deep Learning
			1.1.1 Artificial Intelligence
			1.1.2 Machine Learning
			1.1.3 Deep Learning
			1.1.4 How they Come Together
		1.2 Artificial Intelligence Is Driving Innovation
			1.2.1 Transforming Healthcare
			1.2.2 Protecting Wildlife
			1.2.3 Securing the Environment
		1.3 Tools, Frameworks and Hardware
			1.3.1 Building Intelligent Applications
			1.3.2 Python, Notebooks and Environments
			1.3.3 Pre-Processing
			1.3.4 Machine Learning
			1.3.5 Deep Learning
			1.3.6 Inferencing
		1.4 How this Book Is Organised
		1.5 Who Should Read this Book
		1.6 Summary
		References
Part II: Foundations of Machine Learning
	Chapter 2: Fundamentals of Machine Learning
		2.1 What Is Machine Learning?
			2.1.1 Formal and Non-Formal Definition
			2.1.2 How AI and Machine Learning Differs from Conventional Software Development
				2.1.2.1 Rewriting the Rules
				2.1.2.2 Intelligent Decision Making
		2.2 Machine Learning Tribes
			2.2.1 Connectionists
			2.2.2 Evolutionists
			2.2.3 Bayesians
			2.2.4 Symbolists
			2.2.5 Analogists
		2.3 Data Management
			2.3.1 Data Types and Data Objects
				2.3.1.1 Numerical
				2.3.1.2 Textual
				2.3.1.3 Categorical
				2.3.1.4 Timeseries
			2.3.2 Data Structure
				2.3.2.1 Data Objects
			2.3.3 Datasets
			2.3.4 Exploratory Data Analysis
				2.3.4.1 What Is Exploratory Data Analysis
				2.3.4.2 Data Distributions
				2.3.4.3 Validate Assumptions
				2.3.4.4 Feature Engineering
		2.4 Learning Problems
			2.4.1 Supervised Machine Learning
			2.4.2 Semi-Supervised Machine Learning
			2.4.3 Un-Supervised Machine Learning
			2.4.4 Regression
			2.4.5 Reinforcement Learning
		2.5 Evaluating Machine Learning Models
		2.6 Summary
		References
	Chapter 3: Supervised Learning
		3.1 Basic Concepts
		3.2 Supervised Learning Tasks
			3.2.1 Data Extraction
			3.2.2 Data Preparation
				3.2.2.1 Data Size
				3.2.2.2 Missing Data
				3.2.2.3 Textual Data
					One Hot Encoding
				3.2.2.4 Value Ranges (Normalisation and Scaling)
					Scaling
					Normalisation
					Standardisation
				3.2.2.5 Distribution
				3.2.2.6 Class Balance
				3.2.2.7 Correlation Between Features
			3.2.3 Feature Engineering
				3.2.3.1 Feature Selection
				3.2.3.2 Dimensionality Reduction
			3.2.4 Selecting a Training Algorithm
		3.3 Supervised Algorithms
			3.3.1 Linear Regression
			3.3.2 Logistic Regression
			3.3.3 Linear Discriminate Analysis
			3.3.4 Support Vector Machine
			3.3.5 Random Forest
			3.3.6 Naive Bayes
			3.3.7 K-Nearest Neighbours
		3.4 Summary
		References
	Chapter 4: Un-Supervised Learning
		4.1 Basic Concepts
		4.2 Clustering
			4.2.1 Hierarchical Clustering
			4.2.2 K-Means
			4.2.3 Mixture Models
			4.2.4 DBSCAN
			4.2.5 Optics Algorithm
		4.3 Principal Component Analysis
		4.4 Association Rule Mining
		4.5 Summary
		References
	Chapter 5: Performance Evaluation Metrics
		5.1 Introduction to Model Evaluation
			5.1.1 Evaluation Challenges
			5.1.2 Taxonomy of Classifier Evaluation Metrics
		5.2 Classification Accuracy
		5.3 Train, Test and Validation Sets
		5.4 Underfitting and Overfitting
		5.5 Supervised Learning Evaluation Metrics
			5.5.1 Confusion Matrix
				5.5.1.1 Accuracy
				5.5.1.2 Precision
				5.5.1.3 Recall (Sensitivity)
				5.5.1.4 Specificity
				5.5.1.5 False Positive Rate
				5.5.1.6 F1-Score
			5.5.2 Receiver Operating Characteristic
			5.5.3 Regression Metrics
				5.5.3.1 Mean Square Error (MSE)
				5.5.3.2 MAE
				5.5.3.3 R2 (Coefficient of Determination)
		5.6 Probability Scoring Methods
			5.6.1 Log Loss Score
			5.6.2 Brier Score
		5.7 Cross-Validation
			5.7.1 Challenge of Evaluating Classifiers
			5.7.2 K-Fold Cross-Validation
		5.8 Un-Supervised Learning Evaluation Metric
			5.8.1 Elbow Method
			5.8.2 Davies-Bouldin Index
			5.8.3 Dunn Index
			5.8.4 Silhouette Coefficient
		5.9 Summary
		References
Part III: Deep Learning Concepts and Techniques
	Chapter 6: Introduction to Deep Learning
		6.1 So what´s the Difference Between DL and ML?
		6.2 Introduction to Deep Learning
		6.3 Artificial Neural Networks
			6.3.1 Perceptrons
			6.3.2 Neural Networks
			6.3.3 Activation Functions
			6.3.4 Multi-Class Classification Considerations
			6.3.5 Cost Functions and Optimisers
			6.3.6 Backpropagation
			6.3.7 The Vanishing Gradient
			6.3.8 Weight Initialisation
			6.3.9 Regularisation
		6.4 Convolutional Neural Networks
			6.4.1 Image Filters and Kernels
			6.4.2 Convolutional Layers
			6.4.3 Pooling Layers
			6.4.4 Transfer Learning
		6.5 Summary
		References
	Chapter 7: Image Classification and Object Detection
		7.1 Hardware Accelerated Deep Learning
			7.1.1 Training and Associated Hardware
				7.1.1.1 Development Systems
				7.1.1.2 Training Systems
				7.1.1.3 Inferencing Systems
			7.1.2 Tensor Processing Unit (TPU)
			7.1.3 Other Hardware Considerations
		7.2 Object Recognition
			7.2.1 Image Classification
			7.2.2 Object Detection
			7.2.3 Semantic Segmentation
			7.2.4 Object Segmentation
		7.3 Model Architectures
			7.3.1 Single Shot Detector (SSD)
			7.3.2 YOLO Family
			7.3.3 R-CNN
			7.3.4 Fast-RCNN
			7.3.5 Faster-RCNN
			7.3.6 EfficientNet
			7.3.7 Comparing Architectures
				7.3.7.1 Key Findings
				7.3.7.2 Most Accurate
				7.3.7.3 Fastest
		7.4 Evaluation Metrics
			7.4.1 Confidence Score
			7.4.2 Intersection over Union
			7.4.3 Mean Average Precision (mAP)
		7.5 Summary
		References
	Chapter 8: Deep Learning Techniques for Time Series Modelling
		8.1 Introduction to Time-Series Data
		8.2 Recurrent Neural Network
			8.2.1 Developing RNNs for Time Series Forecasting
		8.3 Long-Term Short-Term Memory
		8.4 Gated Recurrent Unit
		8.5 One Dimensional Convolutional Neural Network
		8.6 Summary
		References
	Chapter 9: Natural Language Processing
		9.1 Introduction to Natural Language Processing
			9.1.1 Tokenisation
			9.1.2 Stemming
			9.1.3 Lemmatization
			9.1.4 Stop Words
			9.1.5 Phrase Matching and Vocabulary
		9.2 Text Classification
			9.2.1 Text Feature Extraction
		9.3 Sentiment Analysis
		9.4 Topic Modelling
			9.4.1 Latent Semantic Analysis (LSA)
			9.4.2 Latent Dirichlet Allocation
			9.4.3 Non-negative Matrix Factorization
		9.5 Deep Learning for NLP
			9.5.1 Word Embeddings
			9.5.2 Word Embedding Algorithms
				9.5.2.1 Embedding Layer
				9.5.2.2 Word2Vec
				9.5.2.3 GloVe
				9.5.2.4 Natural Language Understanding and Generation
		9.6 Real-World Applications
			9.6.1 Chat Bots
			9.6.2 Smart Speakers
		9.7 Summary
		References
	Chapter 10: Deep Generative Models
		10.1 Autoencoders
			10.1.1 Autoencoder Basics
			10.1.2 Autoencoder for Dimensionality Reduction
			10.1.3 Autoencoder for Images
			10.1.4 Stacked Autoencoders
			10.1.5 Generative Adversarial Networks (GANS)
				10.1.5.1 GANs Network Architectures
		10.2 Summary
		References
	Chapter 11: Deep Reinforcement Learning
		11.1 What Is Reinforcement Learning?
		11.2 Reinforcement Learning Definitions
		11.3 Domain Selection for Reinforcement Learning
		11.4 State-Action Pairs and Complex Probability Distributions of Reward
		11.5 Neural Networks and Reinforcement Learning
		11.6 The Deep Reinforcement Learning Process
		11.7 Practical Applications of Deep Reinforcement Learning
		11.8 Summary
		References
Part IV: Enterprise Machine Learning
	Chapter 12: Accelerated Machine Learning
		12.1 Introduction
			12.1.1 CPU/GPU Based Clusters
		12.2 CPU Accelerated Computing
			12.2.1 Distributed Accelerated Computing Frameworks
				12.2.1.1 Local Vs Distributed
				12.2.1.2 Benefits of Scaling Out
				12.2.1.3 Hadoop
				12.2.1.4 Apache Spark
		12.3 Introduction to DASK
			12.3.1 DASK Arrays
			12.3.2 Scikit Learn and DASK Integration (DASK ML)
			12.3.3 Scikit Learn Joblib
		12.4 GPU Computing
			12.4.1 Introduction to GPU Hardware
			12.4.2 Introduction to NVIDIA Accelerated Computing
			12.4.3 CUDA
			12.4.4 CUDA Accelerated Computing Libraries
		12.5 RAPIDS
			12.5.1 cuDF Analytics
			12.5.2 cuML Machine Learning
			12.5.3 cuGraph Graph Analytics
			12.5.4 Apache Arrow
		12.6 Summary
		References
	Chapter 13: Deploying and Hosting Machine Learning Models
		13.1 Introduction to Deployment
			13.1.1 Why Is Model Deployment Important
			13.1.2 Enabling MLOps
			13.1.3 MLOps Frameworks
			13.1.4 MLOps Application Programmable Interfaces API´s
		13.2 Preparing a Model
			13.2.1 Model Formats
				13.2.1.1 ProtoBuf (pb)
				13.2.1.2 ONNX (.ONNX)
				13.2.1.3 Keras h5 (.h5)
				13.2.1.4 TensorFlow SavedModel Format
				13.2.1.5 Scikit-Learn (.pkl)
				13.2.1.6 IOS Platform (.mlmodel)
				13.2.1.7 PyTorch (.pt)
			13.2.2 Freezing and Exporting Models
			13.2.3 Model Optimisation
			13.2.4 Deploying the TFLite Model and Undertaking Inference
		13.3 Web Deployment
			13.3.1 Flask
			13.3.2 Why Use Flask
			13.3.3 Working and Developing in Flask
		13.4 Summary
		References
	Chapter 14: Enterprise Machine Learning Serving
		14.1 Docker
			14.1.1 What Is Docker
			14.1.2 Working with Docker
				14.1.2.1 Using Docker
				14.1.2.2 What´s a Container
				14.1.2.3 Docker Run
				14.1.2.4 Container Lifecycle
				14.1.2.5 Building Custom Dockers
			14.1.3 Docker Compose
			14.1.4 Docker Volume and Mount
		14.2 Kubernetes
		14.3 TensorFlow Serving
			14.3.1 Why Use TensorFlow Serving
			14.3.2 TensorFlow Serving on CPU and GPU (NVidia Runtime)
		14.4 Summary
		References