Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Learning framework for Julia
| Date | Stars |
|---|---|
| 2026-07-31 | 1285 |
| 2026-08-01 | 1285 |
| 2026-08-06 | 1285 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
**Update Dec. 2018**: Mocha.jl is now deprecated. The latest version works with Julia v0.6. If you have existing legacy codebase with Mocha that you want to updates for Julia v1.0, the pull request [255](https://github.com/pluskid/Mocha.jl/pull/255) contains fixes for CPU backend only that have all the unit tests passed under Julia v1.0. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels directly in Julia and general auto-differentiation supports, the Mocha codebase becomes excessively old and primitive. Reworking Mocha with new technologies requires some non-trivial efforts, and new exciting solutions already exist nowadays, it is a good time for the retirement of Mocha.jl. If you are interested in doing deep learning with Julia, please check out some alternative packages that are more up-to-date and actively maintained. In particular, there are [Knet.jl](https://github.com/denizyuret/Knet.jl) and [Flux.jl](https://github.com/FluxML/Flux.jl) for pure-Julia solutions, and [MXNet.jl](https://github.com/dmlc/MXNet.jl) and [Tensorflow.jl](https://github.com/malmaud/TensorFlow.jl) for wrapper to existing deep learning systems. # Mocha [](https://travis-ci.org/pluskid/Mocha.jl) [](http://mochajl.readthedocs.org/) [](http://pkg.julialang.org/?pkg=Mocha&ver=0.6) [](https://coveralls.io/r/pluskid/Mocha.jl?branch=master) [](LICENSE.md) <!--[](https://ci.appveyor.com/project/pluskid/mocha-jl)--> [Tutorials](http://mochajl.readthedocs.org/en/latest/#tutorials) | [Documentation](http://mochajl.readthedocs.org/) | [Release Notes](NEWS.md) | [Roadmap](https://github.com/pluskid/Mocha.jl/issues/22) | [Issues](https://github.com/pluskid/Mocha.jl/issues) Mocha is a Deep Learning framework for [Julia](http://julialang.org/), inspired by the C++ framework [Caffe](http://caffe.berkeleyvision.org/). Efficient implementations of general stochastic gradient solvers and common layers in Mocha can be used to train deep / shallow (convolutional) neural networks, with (optional) unsupervised pre-training via (stacked) auto-encoders. Some highlights: - **Modular Architecture**: Mocha has a clean architecture with isolated components like network layers, activation functions, solvers, regularizers, initializers, etc. Built-in components are sufficient for typical deep (convolutional) neural network applications and more are being added in each release. All of them can be easily extended by adding custom sub-types. - **High-level Interface**: Mocha is written in [Julia](http://julialang.org/), a high-level dynamic programming language designed for scientific computing. Combining with the expressive power of Julia and its package eco-system, playing with deep neural networks in Mocha is easy and intuitive. See for example our IJulia Notebook example of [using a pre-trained imagenet model to do image classification](http://nbviewer.ipython.org/github/pluskid/Mocha.jl/blob/master/examples/ijulia/ilsvrc12/imagenet-classifier.ipynb). - **Portability and Speed**: Mocha comes with multiple backends that can be switched transparently. - The *pure Julia backend* is portable -- it runs on any platform that supports Julia. This is reasonably fast on small models thanks to Julia's LLVM-based just-in-time (JIT) compiler and [Performance Annotations](http://julia.readthedocs.org/en/latest/manual/performance-tips/#performance-annotations), and can
Excerpt of 7,696 characters
Read on GitHub
864
34
19
18
17
11
10
5
Scott Lundberg · Microsoft AI · United States
5
5
4
4
4
Alexander Amini
3
Philipp Moritz · Anyscale
3
2
2
2
Ryuichi Yamamoto · @line · Japan
2
Carlo Lucibello · Bocconi University
2
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9e8c8e4a90e680fc, desc:deep learning framework