Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
by Jeremy Howard
On This Page
Description
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 you how to show more train a model on a wide range of tasks using fastai and PyTorch. You ́ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. 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 show lessTags
Recommendations
Members
- Recently Added By
Lists
Best ML and DS textbooks
15 works; 2 members
Author Information
19 Works 169 Members
Classifications
- Genres
- Technology, Nonfiction, General Nonfiction
- DDC/MDS
- 006.312 — Computer science, information & general works Computer science, knowledge & systems Special computer methods (AI, barcoding, VR, web design, social media) Artificial Intelligence Machine Learning Data mining
- LCC
- QA76.9 .D343 .H693 — Science Mathematics Mathematics Instruments and machines Calculating machines Electronic computers. Computer science
- BISAC
Statistics
- Members
- 51
- Popularity
- 591,117
- Languages
- English
- Media
- Paper, Ebook
- ISBNs
- 4
- ASINs
- 1
