Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
Overview & Synopsis
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.
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Document Information
| Document Title | Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD |
|---|---|
| Author / Organization | Jeremy Howard and Sylvain Gugger |
| Subject & Discipline | Deep Learning and Neural Networks, Python Programming, Machine Learning, Artificial Intelligence, Data Science |
| Categories | Deep Learning and Neural Networks |
| Publication Year | 2020 |
| ISBN | 1492045527 |
| Resource Verification | External Reading Page / Repository |
| Alternative Mirrors & Links | |
| Original Host Website |
FreeComputerBooks.com
(freecomputerbooks.com)
|
| File Format | application/pdf (Portable Document Format) |
| Language | ENGLISH / SPANISH / KOREAN / CHINESE / BENGALI / INDONESIAN / ITALIAN / PORTUGUESE / VIETNAMESE |
| Catalog ID | DOC-93344 |
| Original Source Link | Visit Source Page on freecomputerbooks.com » |
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