Deep learning for coders with fastai and PyTorch: AI applications without a phd/ Jeremy Howard and Sylvain Gugger

Includes index

Your Deep Learning Journey -- From Model to Production -- Data Ethics -- 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 -- A Language Model from Scratch -- Convolutional Neural Networks -- ResNets -- Application Architectures Deep Dive -- The Training Process -- A Neural Net from the Foundations -- CNN Interpretation with CAM -- A fastai Learner from Scratch -- Concluding Thoughts -- Creating a Blog -- Data Project Checklist

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show the reader how to train a model on a wide range of tasks using fastai and PyTorch. The reader will also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.