Surprised (and delighted!) to see this pop up on the front page of HN today! This course is designed to teach proficient coders the
practice of deep learning, that is: how to train accurate models; how to test and debug models; and key deep learning concepts. It covers applications in vision, natural language processing, tabular data, and recommendation systems.
If you're interested in diving deeper into the papers and math behind the scenes, as well as coding from the lowest levels (right down to the compiler level), you'll be interested in "Deep Learning from the Foundations", which is coming out in 2 weeks. The last two lessons are co-taught with Chris Lattner (creator of Swift, LLVM, and Clang).
If you want to understand the underlying linear algebra implementation details, have a look at "Computational Linear Algebra for Coders": https://github.com/fastai/numerical-linear-algebra
If you want to learn about decision trees, random forests, linear regression, validation sets, etc, try "Introduction to Machine Learning for Coders": https://course18.fast.ai/ml
(All are free and have no ads. They are provided as a service to the community.)
Let me know if you have any questions!
If you're interested in diving deeper into the papers and math behind the scenes, as well as coding from the lowest levels (right down to the compiler level), you'll be interested in "Deep Learning from the Foundations", which is coming out in 2 weeks. The last two lessons are co-taught with Chris Lattner (creator of Swift, LLVM, and Clang).
If you want to understand the underlying linear algebra implementation details, have a look at "Computational Linear Algebra for Coders": https://github.com/fastai/numerical-linear-algebra
If you want to learn about decision trees, random forests, linear regression, validation sets, etc, try "Introduction to Machine Learning for Coders": https://course18.fast.ai/ml
(All are free and have no ads. They are provided as a service to the community.)
Let me know if you have any questions!