Displaying 1 to 20 from 25 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.

Forge - A neural network toolkit for Metal

Forge is a collection of helper code that makes it a little easier to construct deep neural networks using Apple's MPSCNN framework. Conversion functions. MPSCNN uses MPSImages and MTLTextures for everything, often using 16-bit floats. But you probably want to work with Swift [Float] arrays. Forge's conversion functions make it easy to work with Metal images and textures.

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

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.

tfjs-core - WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.

NOTE: Building on the momentum of deeplearn.js, we have joined the TensorFlow family and we are starting a new ecosystem of libraries and tools for Machine Learning in Javascript, called TensorFlow.js. This repo moved from PAIR-code/deeplearnjs to tensorflow/tfjs-core. A part of the TensorFlow.js ecosystem, this repo hosts @tensorflow/tfjs-core, the TensorFlow.js Core API, which provides low-level, hardware-accelerated linear algebra operations and an eager API for automatic differentiation.

pix2code - pix2code: Generating Code from a Graphical User Interface Screenshot

Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies). The following software is shared for educational purposes only. The author and its affiliated institution are not responsible in any manner whatsoever for any damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of the use or inability to use this software.

Bender - Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

Bender is an abstraction layer over MetalPerformanceShaders useful for working with neural networks. Bender is an abstraction layer over MetalPerformanceShaders which is used to work with neural networks. It is of growing interest in the AI environment to execute neural networks on mobile devices even if the training process has been done previously. We want to make it easier for everyone to execute pretrained networks on iOS.

Kur - Descriptive Deep Learning

Kur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows.

DeepLearning.scala - A simple library for creating complex neural networks

DeepLearning.scala is a simple library for creating complex neural networks from object-oriented and functional programming constructs. Like other deep learning toolkits, DeepLearning.scala allows you to build neural networks from mathematical formulas. It supports floats, doubles, GPU-accelerated N-dimensional arrays, and calculates derivatives of the weights in the formulas.

sockeye - Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post (2017): Sockeye: A Toolkit for Neural Machine Translation. In eprint arXiv:cs-CL/1712.05690.If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com.

emotion-recognition-neural-networks - Emotion recognition using DNN with tensorflow

This repository is the out project about mood recognition using convolutional neural network for the course Seminar Neural Networks at TU Delft. We use the FER-2013 Faces Database, a set of 28,709 pictures of people displaying 7 emotional expressions (angry, disgusted, fearful, happy, sad, surprised and neutral).

twitter-sent-dnn - Deep Neural Network for Sentiment Analysis on Twitter

It returns a sentiment index ranging from 0 (negative sentiment) to 1 (positive sentiment). Please refer to A Convolutional Neural Network for Modelling Sentences for more information about the algorithm.

MachineLearningSamples-BiomedicalEntityExtraction - MachineLearningSamples-BiomedicalEntityExtraction

This real-world scenario focuses on how a large amount of unstructured unlabeled data corpus such as PubMed article abstracts can be analyzed to train a domain-specific word embedding model. Then the output embeddings are considered as automatically generated features to train a neural entity extraction model using Keras with TensorFlow deep learning framework as backend and a small amoht of labeled data.The detailed documentation for this scenario including the step-by-step walk-through: https://review.docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-tdsp-biomedical-recognition.

deep_bait - Running the most popular deep learning frameworks on Azure Batch AI

This repo contains everything you need to run some of the most popular deep learning frameworks on Batch AI. Batch AI is a service that allows you to run various machine learning workloads on clusters of VMs. For more details on the service please look here.This project uses anaconda-project and makefiles to create the environment, download the data and prepare all necessary artifacts.

dbn-cuda - GPU accelerated Deep Belief Network

In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples in an unsupervised way, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors on inputs. After this learning step, a DBN can be further trained in a supervised way to perform classification.

Supervised-End-to-end-Weight-sharing-for-StarCraft-II - StarCraft 2 AI Workshop

Sorry in advance, the code is far from clean and not very well-structured. Please keep in mind that the goal of the workshop was to implement something in less than 26 hours from 1pm on Saturday January 20th to 3pm on Sunday January 21st. In parentheses is the version of these packages I used during this workshop but you should be able to run my code using other releases with minor changes. You can read Niel's great post for a step by step walkthrough on setting up your environment.

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.

NueralNets - Fortran Based Nueral Networks

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