Practical Deep Learning : A Python-Based Introduction

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning , it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: • How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines • How neural networks work and how they’re trained • How to use convolutional neural networks • How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects. Brief Contents Contents in Detail Foreword Acknowledgments Introduction Chapter 1: Getting Started Chapter 2: Using Python Chapter 3: Using NumPy Chapter 4: Working with Data Chapter 5: Building Datasets Chapter 6: Classical Machine Learning Chapter 7: Experiments with Classical Models Chapter 8: Introduction to Neural Networks Chapter 9: Training a Neural Network Chapter 10: Experiments with Neural Networks Chapter 11: Evaluating Models Chapter 12: Introduction to Convolutional Neural Networks Chapter 13: Experiments with Keras and MNIST Chapter 14: Experiments with CIFAR-10 Chapter 15: A Case Study: Classifying Audio Samples Chapter 16: Going Further Index 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 This Book Is For People With No Experience With Machine Learning And Who Are Looking For An Intuition-based, Hands-on Introduction To Deep Learning Using Python. Deep Learning For Complete Beginners: A Python-based Introduction Is For Complete Beginners In Machine Learning. It Introduces Fundamental Concepts Such As Classes And Labels, Building A Dataset, And What A Model Is And Does Before Presenting Classic Machine Learning Models, Neural Networks, And Modern Convolutional Neural Networks. Experiments In Python--working With Leading Open-source Toolkits And Standard Datasets--give The Reader Hands-on Experience With Each Model And Help Them Build Intuition About How To Transfer The Examples In The Book To Their Own Projects. Readers Start With An Introduction To The Python Language And The Numpy Extension That Is Ubiquitous In Machine Learning. Prominent Toolkits, Like Sklearn And Keras/tensorflow Are Used As The Backbone To Enable Readers To Focus On The Elements Of Machine Learning Without The Burden Of Writing Implementations From Scratch. An Entire Chapter On Evaluating The Performance Of Models Gives The Reader The Knowledge Necessary To Understand Claims On Performance And To Know Which Models Are Working Well And Which Are Not. The Book Culminates By Presenting Convolutional Neural Networks As An Introduction To Modern Deep Learning. Understanding How These Networks Work And How They Are Affected By Parameter Choices Leaves The Reader With The Core Knowledge Necessary To Dive Into The Larger, Ever-changing World Of Deep Learning. Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.If you've been curious about artificial intelligence and machine learning but didn't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further.All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you'll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models'performance.You'll also learn:How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector MachinesHow neural networks work and how they're trainedHow to use convolutional neural networksHow to develop a successful deep learning model from scratch You'll conduct experiments along the way, building to a final case study that incorporates everything you've learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.