Deep learning for coders with fastai and PyTorch : AI applications without a PhD / Jeremy Howard and Sylvain Gugger; [foreword by Soumith Chintala].

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Includes index

Part 1. Deep Learning Journey. Your Deep Learning Journey -- From Model to Production -- Data Ethics -- Part 2. Understanding fastai's Applications. Under the Hood: Training a Digit Classifier -- Image Classification --Other Computer Vision Problems -- Training a State-of-the-Art Model -- Collaborative Filtering Deep Dive -- Tabular Modeling Deep Dive -- NLP Deep Dive: RNNs -- Data Munging with fastai's Mid-Level API -- Part 3. Foundations of Deep Learning. A Language Model from Scratch -- Convolutional Neural Networks -- ResNets -- Application Architectures Deep Dive -- The Training Process -- Part 4. Deep Learning from Scratch. A Neural Net from the Foundations -- CNN Interpretation with CAM -- A fastai Learner from Scratch -- Concluding Thoughts.

Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks-including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions.

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