Insights Gleaned from the "Deep Learning for Coders" Manual
In an exciting development for the tech community, a new course, book, and software libraries related to deep learning have been released. The book, titled "Deep Learning for Coders with fastai and PyTorch," co-authored by Jeremy Howard and Sylvain Gugger, aims to make deep learning accessible to programmers with minimal mathematical background.
The book, which features a foreword by Soumith Chintala, co-creator of PyTorch, is designed to help individuals, regardless of the nature of the problem, put deep learning to good use. Soumith Chintala, in his foreword, expresses his support for the book, emphasizing its potential to democratize deep learning.
The 500-page book covers the latest advances in computer vision, natural language processing, and foundational math. It is intended to be a delightful ride for readers, using simple words to introduce every concept. The authors, Jeremy and Sylvain, have built a course that makes deep learning accessible to people who know basic programming.
The book is not just a theoretical guide but also provides practical insights on shipping ideas to production. It addresses the common assumption among engineers and programmers that a certain level of knowledge about GPUs and obscure tools is required to understand deep learning. The book, therefore, provides a fast and easy way to get started with powerful deep learning software.
For those who find reading state-of-the-art research on deep learning challenging due to complex language and mathematical notation, this book offers a more approachable alternative. It simplifies the learning process, making it possible to understand the basics of deep learning in a few weeks.
The authors' approach to deep learning has proven successful, with their course graduating hundreds of thousands of eager learners who have become great practitioners. The "our website" community, thousands of practitioners online, can be treated as an extended family, where individuals can talk and ideate small and big solutions.
Deep learning is a widely useful technique in various fields such as computer vision, robotics, healthcare, physics, biology, and beyond. With a vast body of knowledge on deep learning, spanning three decades of theory, techniques, and tooling, this book serves as an excellent resource for anyone looking to delve into this exciting field.
- The new course, book, and software libraries related to deep learning, titled "Deep Learning for Coders with fastai and PyTorch," aim to make deep learning accessible to programmers.
- The book, co-authored by Jeremy Howard and Sylvain Gugger, covers the latest advances in computer vision, natural language processing, and foundational math.
- Soumith Chintala, co-creator of PyTorch, expresses his support for the book in his foreword, emphasizing its potential to democratize deep learning.
- The authors have built a course that uses simple words to introduce every concept, and it provides practical insights on shipping ideas to production.
- For those who find reading state-of-the-art research on deep learning challenging, this book offers a more approachable alternative, simplifying the learning process.
- The authors' approach to deep learning has proven successful, with their course graduating hundreds of thousands of eager learners who have become great practitioners, and their online community can be treated as an extended family.
