Deep Learning is often viewed as the exclusive domain of math PhDs and big tech companies.
Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest Deep Learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how Deep Learning models work Discover how to turn your models into web Applications Implement Deep Learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala.
You\'ll also dive progressively further into Deep Learning theory to gain a complete understanding of the algorithms behind the scenes.
Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using Fastai and PyTorch.
How? with fastai, the first library to provide a consistent interface to the most frequently used Deep Learning applications.
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.
Deep Learning is often viewed as the exclusive domain of math PhDs and big tech companies
