Practical Deep Learning: A Python-Based Introduction

Brief Contents
Contents in Detail
Foreword
Acknowledgments
Introduction
	Who Is This Book For?
	What Can You Expect to Learn?
	About This Book
Chapter 1: Getting Started
	The Operating Environment
		NumPy
		scikit-learn
		Keras with TensorFlow
	Installing the Toolkits
	Basic Linear Algebra
		Vectors
		Matrices
		Multiplying Vectors and Matrices
	Statistics and Probability
		Descriptive Statistics
		Probability Distributions
		Statistical Tests
	Graphics Processing Units
	Summary
Chapter 2: Using Python
	The Python Interpreter
	Statements and Whitespace
	Variables and Basic Data Structures
		Representing Numbers
		Variables
		Strings
		Lists
		Dictionaries
	Control Structures
		if-elif-else Statements
		for Loops
		while Loops
		break and continue Statements
		with Statement
		Handling Errors with try-except Blocks
	Functions
	Modules
	Summary
Chapter 3: Using NumPy
	Why NumPy?
		Arrays vs. Lists
		Testing Array and List Speed
	Basic Arrays
		Defining an Array with np.array
		Defining Arrays with 0s and 1s
	Accessing Elements in an Array
		Indexing into an Array
		Slicing an Array
		The Ellipsis
	Operators and Broadcasting
	Array Input and Output
	Random Numbers
	NumPy and Images
	Summary
Chapter 4: Working with Data
	Classes and Labels
	Features and Feature Vectors
		Types of Features
		Feature Selection and the Curse of Dimensionality
	Features of a Good Dataset
		Interpolation and Extrapolation
		The Parent Distribution
		Prior Class Probabilities
		Confusers
		Dataset Size
	Data Preparation
		Scaling Features
		Missing Features
	Training, Validation, and Test Data
		The Three Subsets
		Partitioning the Dataset
		k-Fold Cross Validation
	Look at Your Data
		Searching for Problems in the Data
		Cautionary Tales
	Summary
Chapter 5: Building Datasets
	Irises
	Breast Cancer
	MNIST Digits
	CIFAR-10
	Data Augmentation
		Why Should You Augment Training Data?
		Ways to Augment Training Data
		Augmenting the Iris Dataset
		Augmenting the CIFAR-10 Dataset
	Summary
Chapter 6: Classical Machine Learning
	Nearest Centroid
	k-Nearest Neighbors
	Naïve Bayes
	Decision Trees and Random Forests
		Recursion Primer
		Building Decision Trees
		Random Forests
	Support Vector Machines
		Margins
		Support Vectors
		Optimization
		Kernels
	Summary
Chapter 7: Experiments with Classical Models
	Experiments with the Iris Dataset
		Testing the Classical Models
		Implementing a Nearest Centroid Classifier
	Experiments with the Breast Cancer Dataset
		Two Initial Test Runs
		The Effect of Random Splits
		Adding k-fold Validation
		Searching for Hyperparameters
	Experiments with the MNIST Dataset
		Testing the Classical Models
		Analyzing Runtimes
		Experimenting with PCA Components
		Scrambling Our Dataset
	Classical Model Summary
		Nearest Centroid
		k-Nearest Neighbors
		Naïve Bayes
		Decision Trees
		Random Forests
		Support Vector Machines
	When to Use Classical Models
		Handling Small Datasets
		Dealing with Reduced Computational Requirements
		Having Explainable Models
		Working with Vector Inputs
	Summary
Chapter 8: Introduction to Neural Networks
	Anatomy of a Neural Network
		The Neuron
		Activation Functions
		Architecture of a Network
		Output Layers
		Representing Weights and Biases
	Implementing a Simple Neural Network
		Building the Dataset
		Implementing the Neural Network
		Training and Testing the Neural Network
	Summary
Chapter 9: Training a Neural Network
	A High-Level Overview
	Gradient Descent
		Finding Minimums
		Updating the Weights
	Stochastic Gradient Descent
		Batches and Minibatches
		Convex vs. Nonconvex Functions
		Ending Training
		Updating the Learning Rate
		Momentum
	Backpropagation
		Backprop, Take 1
		Backprop, Take 2
	Loss Functions
		Absolute and Mean Squared Error Loss
		Cross-Entropy Loss
	Weight Initialization
	Overfitting and Regularization
		Understanding Overfitting
		Understanding Regularization
		L2 Regularization
		Dropout
	Summary
Chapter 10: Experiments with Neural Networks
	Our Dataset
	The MLPClassifier Class
	Architecture and Activation Functions
		The Code
		The Results
	Batch Size
	Base Learning Rate
	Training Set Size
	L2 Regularization
	Momentum
	Weight Initialization
	Feature Ordering
	Summary
Chapter 11: Evaluating Models
	Definitions and Assumptions
	Why Accuracy Is Not Enough
	The 2 x 2 Confusion Matrix
	Metrics Derived from the 2 x 2 Confusion Matrix
		Deriving Metrics from the 2 x 2 Table
		Using Our Metrics to Interpret Models
	More Advanced Metrics
		Informedness and Markedness
		F1 Score
		Cohen\'s Kappa
		Matthews Correlation Coefficient
		Implementing Our Metrics
	The Receiver Operating Characteristics Curve
		Gathering Our Models
		Plotting Our Metrics
		Exploring the ROC Curve
		Comparing Models with ROC Analysis
		Generating an ROC Curve
		The Precision–Recall Curve
	Handling Multiple Classes
		Extending the Confusion Matrix
		Calculating Weighted Accuracy
		Multiclass Matthews Correlation Coefficient
	Summary
Chapter 12: Introduction to Convolutional Neural Networks
	Why Convolutional Neural Networks?
	Convolution
		Scanning with the Kernel
		Convolution for Image Processing
	Anatomy of a Convolutional Neural Network
		Different Types of Layers
		Passing Data Through the CNN
	Convolutional Layers
		How a Convolution Layer Works
		Using a Convolutional Layer
		Multiple Convolutional Layers
		Initializing a Convolutional Layer
	Pooling Layers
	Fully Connected Layers
	Fully Convolutional Layers
	Step by Step
	Summary
Chapter 13: Experiments with Keras and MNIST
	Building CNNs in Keras
		Loading the MNIST Data
		Building Our Model
		Training and Evaluating the Model
		Plotting the Error
	Basic Experiments
		Architecture Experiments
		Training Set Size, Minibatches, and Epochs
		Optimizers
	Fully Convolutional Networks
		Building and Training the Model
		Making the Test Images
		Testing the Model
	Scrambled MNIST Digits
	Summary
Chapter 14: Experiments with CIFAR-10
	A CIFAR-10 Refresher
	Working with the Full CIFAR-10 Dataset
		Building the Models
		Analyzing the Models
	Animal or Vehicle?
	Binary or Multiclass?
	Transfer Learning
	Fine-Tuning a Model
		Building Our Datasets
		Adapting Our Model for Fine-Tuning
		Testing Our Model
	Summary
Chapter 15: A Case Study: Classifying Audio Samples
	Building the Dataset
		Augmenting the Dataset
		Preprocessing Our Data
	Classifying the Audio Features
		Using Classical Models
		Using a Traditional Neural Network
		Using a Convolutional Neural Network
	Spectrograms
	Classifying Spectrograms
		Initialization, Regularization, and Batch Normalization
		Examining the Confusion Matrix
	Ensembles
	Summary
Chapter 16: Going Further
	Going Further with CNNs
	Reinforcement Learning and Unsupervised Learning
	Generative Adversarial Networks
	Recurrent Neural Networks
	Online Resources
	Conferences
	The Book
	So Long and Thanks for All the Fish
Index