- It took me 4 days to learn Julia to a working level. (Yes I have been coding for a while so it was way easier than I thought it would be)
- I am absolutely in love with the syntax
- Love the way you can write \gamma+\delta-y\hat to get γ+δ+ŷ
- Learning Julia
- A personal compilation on Deep Learning
- Amazing libraries I loved
- InterOP between other languages
- Communities I found helpful
- Personal tips
- Personal Folders in this repository
- Contribute
- Official tutorial
- A helpful tutorial
- Bunch of videos
- JuliaCon 2019
- JuliaCon 2018
- JuliaCon 2017
- JuliaCon 2016
- JuliaCon 2015
- JuliaCon 2014
- Tedx Talk
- Installation
- JuliaLang Docs - Simply amazing
- Deep learning basic models
- Neural differential equations
- Rosetta challenges
- Type greek and emojis!! (and others)
- Website to find popular libraries
- Core Library
- Neural differential equations/others
- SciML
- Stats Library
- Scikit learn
- Deep learning - FluxML
- Quantum Library
- Sparse matrix/graphs
- Parallel programming
- Computer vision models
- Javascript integration
- Automatic differentiation Library
- Tensorflow
- Want to learn DL??
- Have no idea where to start?
- Here is a personal compilation of links that have helped me get where I am today :)
- Important links
- Lenet5 : Flux ML implementation of Lenet5
- LeCun, Y. (2015). LeNet-5, convolutional neural networks. URL: http://yann. lecun. com/exdb/lenet, 20, 5.
- trying : Just my tests
- vgg : Flux ML implementation of vgg16
- This has implementations of DCGANs, A test of neural ODEs in VAEs (WIP)
- Something special and a work in progress. Using Ordinary differential equations to streamline networks
- paper
- Something fun I am working on. I do like the FluxML API but I wanted to make it better and more generalized
- Yes it is a very new language so it will be hard to find Tutorials
- Use it if you want to do scientific computing and other languages are too slow
- I love the syntax even more than I did python's
- Do not shift to this as your primary language yet (Maybe in a few years)
- Do look at the repositories of the packages you are using. Julia is extremely easy to understand and you will be better off looking at existing codebases
- Some things dont exist yet so feel free to use python/R wrappers to get them done
- You can get stuff directly from cran/pypi
- Use R/python libraries to get datasets
- Contribute heavily!!!!
- Can I add to this list?? YES
- Please feel free to contribute to this repo by sending a PR.
- Anything that you wish to add which personally helped you is welcome :)
- If its your own package, please do add a note on usabilty if you are dropping a PR