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.
Cover Page Title Page Copyright Page Dedication About the Author About the Technical Reviewer BRIEF CONTENTS CONTENTS IN DETAIL FOREWORD ACKNOWLEDGMENTS INTRODUCTION Who Is This Book For? What Can You Expect to Learn? About This Book 1 GETTING STARTED The Operating Environment Installing the Toolkits Basic Linear Algebra Statistics and Probability Graphics Processing Units Summary 2 USING PYTHON The Python Interpreter Statements and Whitespace Variables and Basic Data Structures Control Structures Functions Modules Summary 3 USING NUMPY Why NumPy? Basic Arrays Accessing Elements in an Array Operators and Broadcasting Array Input and Output Random Numbers NumPy and Images Summary 4 WORKING WITH DATA Classes and Labels Features and Feature Vectors Features of a Good Dataset Data Preparation Training, Validation, and Test Data Look at Your Data Summary 5 BUILDING DATASETS Irises Breast Cancer MNIST Digits CIFAR-10 Data Augmentation Summary 6 CLASSICAL MACHINE LEARNING Nearest Centroid k-Nearest Neighbors Naïve Bayes Decision Trees and Random Forests Support Vector Machines Summary 7 EXPERIMENTS WITH CLASSICAL MODELS Experiments with the Iris Dataset Experiments with the Breast Cancer Dataset Experiments with the MNIST Dataset Classical Model Summary When to Use Classical Models Summary 8 INTRODUCTION TO NEURAL NETWORKS Anatomy of a Neural Network Implementing a Simple Neural Network Summary 9 TRAINING A NEURAL NETWORK A High-Level Overview Gradient Descent Stochastic Gradient Descent Backpropagation Loss Functions Weight Initialization Overfitting and Regularization Summary 10 EXPERIMENTS WITH NEURAL NETWORKS Our Dataset The MLPClassifier Class Architecture and Activation Functions Batch Size Base Learning Rate Training Set Size L2 Regularization Momentum Weight Initialization Feature Ordering Summary 11 EVALUATING MODELS Definitions and Assumptions Why Accuracy Is Not Enough The 2 × 2 Confusion Matrix Metrics Derived from the 2 × 2 Confusion Matrix More Advanced Metrics The Receiver Operating Characteristics Curve Handling Multiple Classes Summary 12 INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS Why Convolutional Neural Networks? Convolution Anatomy of a Convolutional Neural Network Convolutional Layers Pooling Layers Fully Connected Layers Fully Convolutional Layers Step by Step Summary 13 EXPERIMENTS WITH KERAS AND MNIST Building CNNs in Keras Basic Experiments Fully Convolutional Networks Scrambled MNIST Digits Summary 14 EXPERIMENTS WITH CIFAR-10 A CIFAR-10 Refresher Working with the Full CIFAR-10 Dataset Animal or Vehicle? Binary or Multiclass? Transfer Learning Fine-Tuning a Model Summary 15 A CASE STUDY: CLASSIFYING AUDIO SAMPLES Building the Dataset Classifying the Audio Features Spectrograms Classifying Spectrograms Ensembles Summary 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
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