Displaying 1 to 10 from 10 results

gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.

  •    Go

Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with.

incubator-mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

  •    C++

Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

caffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.

  •    C++

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.




NueralNets - Fortran Based Nueral Networks

  •    Fortran

Fortran 95 Based Nueral Networks and Stochastic Gradient Descent. Examples located in /tests directory. To run examples run make from that directory.


Deep-Image-Analogy-PyTorch - Visual Attribute Transfer through Deep Image Analogy in PyTorch!

  •    Jupyter

Unofficial,PyTorch version of Deep Image Analogy.https://arxiv.org/abs/1705.01088. This project focuses on documentation of the project , and simplifying the structure. A blog post on it is coming soon. This project uses Adam as optimizer instead of LBFGS. LBFGS was giving really poor results.





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