Displaying 1 to 18 from 18 results

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

seq2seq - A general-purpose encoder-decoder framework for Tensorflow


A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.The official code used for the Massive Exploration of Neural Machine Translation Architectures paper.

node-tensorflow - Node.js + TensorFlow


TensorFlow is Google's machine learning runtime. It is implemented as C++ runtime, along with Python framework to support building a variety of models, especially neural networks for deep learning. It is interesting to be able to use TensorFlow in a node.js application using just JavaScript (or TypeScript if that's your preference). However, the Python functionality is vast (several ops, estimator implementations etc.) and continually expanding. Instead, it would be more practical to consider building Graphs and training models in Python, and then consuming those for runtime use-cases (like prediction or inference) in a pure node.js and Python-free deployment. This is what this node module enables.

AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research


AirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want.




t81_558_deep_learning - Washington University (in St


Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

pytorch-seq2seq - pytorch-seq2seq is a framework for sequence-to-sequence (seq2seq) models in PyTorch


This is a framework for sequence-to-sequence (seq2seq) models implemented in PyTorch. The framework has modularized and extensible components for seq2seq models, training and inference, checkpoints, etc. This is an alpha release. We appreciate any kind of feedback or contribution. This package requires Python 2.7 or 3.6. We recommend creating a new virtual environment for this project (using virtualenv or conda).

horovod - Distributed training framework for TensorFlow.


Horovod is a distributed training framework for TensorFlow. The goal of Horovod is to make distributed Deep Learning fast and easy to use.Internally at Uber we found that it's much easier for people to understand an MPI model that requires minimal changes to source code than to understand how to set up regular Distributed TensorFlow.


deeplearning-cfn - Distributed Deep Learning on AWS Using CloudFormation (CFN), MXNet and TensorFlow


AWS CloudFormation, which creates and configures Amazon Web Services resources with a template, simplifies the process of setting up a distributed deep learning cluster. The AWS CloudFormation Deep Learning template uses the Amazon Deep Learning AMI (which provides MXNet, TensorFlow, Caffe, Theano, Torch, and CNTK frameworks) to launch a cluster of EC2 instances and other AWS resources needed to perform distributed deep learning. With this template, we continue with our mission to make distributed deep learning easy. AWS CloudFormation creates all resources in the customer account.We've updated the AWS CloudFormation Deep Learning template to add some exciting new features and capabilities.

sparse-structured-attention - Sparse and structured neural attention mechanisms


Efficient implementation of structured sparsity inducing attention mechanisms: fusedmax, oscarmax and sparsemax. Currently available for pytorch v0.2. Requires python (3.6, 3.5, or 2.7), cython, numpy, scipy, scikit-learn, and lightning.

ghiaseddin - Author's implementation of the paper "Deep Relative Attributes" (ACCV 2016)


This repo contains the code for the paper "Deep Relative Attributes" by Yaser Souri, Erfan Noury, Ehsan Adeli Mosabbeb. Deep Relative Attributes by Yaser Souri (@yassersouri), Erfan Noury (@erfannoury), Ehsan Adeli Mosabbeb (@eadeli). ACCV 2016.

realtime-detectron - Real-time Detectron using webcam.


This is a demo project of a real-time Mask R-CNN using Detectron. We will be using consumer grade webcam for capturing the video stream. Here's an example of demo created by reddit's user _sshin_.

latplan - LatPlan : A domain-independent, image-based classical planner


This repository contains the source code of LatPlan. install.sh should install the required libraries on a standard Ubuntu rig. It requires sudo several times. However Python packages are installed in the user directory.

SERT - Semantic Entity Retrieval Toolkit


The Semantic Entity Retrieval Toolkit (SERT) is a collection of neural entity retrieval algorithms. SERT requires Python 3.5 and assorted modules. The trec_eval utility is required for evaluation and the end-to-end scripts. If you wish to train your models on GPGPUs, you will need a GPU compatible with Theano.