Julia: Looks like Python, feels like Lisp, runs like Fortran
As ML models began to need the full power of a programming language,getting Python to scale to ML’s heavy computational demands is far harder than you might expect. Python’s semantics also make it fundamentally difficult to provide model-level parallelism or compile models for small devices.
More details:
- Tutorials and information on the Julia language for MIT numerical-computation courses
- An Introduction to Julia for Data Science and Scientific Computing
- Introducing Julia wikibook
- Learn Julia the hard way
- Hands-on Julia
- The-Julia-Express
- Julia by Example
- Learn Julia via Examples
- Programming in Julia (Quantitative Economics)
- Machine Learning, Neural Networks, NLP, Speech Recognition and Voice tools
- Julia implementation of the scikit-learn API
- Mocha.jl - Deep Learning framework for Julia
- Merlin.jl - Deep Learning for Julia
- Knet.jl - Koç University deep learning framework
- Julia package for text analysis
- MXNet Julia Package - flexible and efficient deep learning in Julia
- A Julia wrapper for TensorFlow
- Flux is the ML library that doesn't make you tensor
- A julia wrapper for Keras