Inside Deep Learning: Math Algorithms Models

Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems.In Inside Deep Learning, you will learn how to:Implement deep learning with PyTorchSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAdapt existing PyTorch code to solve new problemsInside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you’ll dive into math, theory, and practical applications. Everything is clearly explained in plain English.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyDeep learning doesn’t have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don’t have to be a mathematics expert or a senior data scientist to grasp what’s going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence.About the bookInside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You’ll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware!What’s insideSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAbout the readerFor Python programmers with basic machine learning skills.About the authorEdward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library.Table of ContentsPART 1 FOUNDATIONAL METHODS1 The mechanics of learning2 Fully connected networks3 Convolutional neural networks4 Recurrent neural networks5 Modern training techniques6 Common design building blocksPART 2 BUILDING ADVANCED NETWORKS7 Autoencoding and self-supervision8 Object detection9 Generative adversarial networks10 Attention mechanisms11 Sequence-to-sequence12 Network design alternatives to RNNs13 Transfer learning14 Advanced building blocks