AI Applications Without a PhD
Sylvain Gugger / Jeremy Howard
résumé
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.
contents
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
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