Best Julia library for neural networks

Question

I have been using this library for basic neural network construction and analysis.

However, it does not have support for building multi-layered neural networks, etc.

So, I would like to know of any nice libraries for doing advanced neural networks and Deep Learning in Julia.

Answer 1

Mocha.jl - Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe.

Project with good documentation and examples. Can be run on CPU and GPU backend.

Answer 2

MXNet Julia Package - flexible and efficient deep learning in Julia

https://github.com/dmlc/MXNet.jl

Pros

• Fast
• Scales up to multi GPUs and distributed setting with auto parallelism.
• Lightweight, memory efficient and portable to smart devices.
• Automatic Differentiation

Cons

• Doesn't have yet low level operations for algorithm implementation. But they are working on this issue (https://github.com/dmlc/mxnet/issues/586)

Answer 3

As of Oct 2016 there's also a Tensorflow wrapper for Julia:

https://github.com/malmaud/TensorFlow.jl

Answer 4

Just to add a more recent (2019) answer: Flux.

Flux is an elegant approach to machine learning. It's a 100% pure-Julia stack,
and provides lightweight abstractions on top of Julia's native GPU and
AD support. Flux makes the easy things easy while remaining fully hackable.

For example:

model = Chain(
  Dense(768, 128, σ),
  LSTM(128, 256),
  LSTM(256, 128),
  Dense(128, 10),
  softmax)

loss(x, y) = crossentropy(model(x), y)

Flux.train!(loss, data, ADAM(...))

Answer 5

One newer library to look at as well is Knet.jl. It will do things like use GPUs under the hood.

https://github.com/denizyuret/Knet.jl