DL with Julia is a book about how to do various deep learning tasks using the Julia programming language and specifically the Flux.jl package. The intent of the book is to prove that serious deep learning can be done in Julia and that the ecosystem as a whole is ready for the spotlight.
All of the code and resources for this book are stored here on GitHub and deployed to https://deeplearningwithjulia.com.
At the present moment, my focus is on writing materials in the following areas:
- [] Basic Recurrent Neural Networks with Flux.jl (WIP)
- [] CNN Basics with Flux.jl
- [] Transfer Learning for Computer Vision with Flux.jl (WIP)
- [] Solving basic NLP problems with Flux.jl
- [] Preparing and using data with Flux.jl (inspired by out image augmentation assignment somewhat)
- [] Saving and loading machine learning models in Flux.jl
- [] Automatic Differentiation in Flux (https://www.microsoft.com/en-us/research/video/the-simple-essence-of-automatic-differentiation/)