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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.
machine-learning artificial-intelligence neural-network computation-graph differentiation gradient-descent gorgonia deep-learning deeplearning deep-neural-networks automatic-differentiation symbolic-differentiation go-libraryForge - 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.
metal deep-learning deep-neural-networks neural-network ios mobilenets machine-learningincubator-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.
deep-learning deep-neural-networks mxnet distributed-systems machine-learning artificial-intelligenceBender - 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.
machine-learning neural-networks metal apple iphone ios convolutional-neural-networks deep-learning deep-neural-networks residual-networksKur - 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.
deep-learning deep-neural-networks speech-recognition deep-learning-tutorial machine-learning neural-networks neural-network image-recognition speech-to-textDeepLearning.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.
automatic-differentiation deep-neural-networks deep-learning neural-network functional-programming symbolic-computation dsl domain-specific-language machine-learningsockeye - 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.
deep-learning deep-neural-networks mxnet machine-learning machine-translation neural-machine-translation encoder-decoder attention-mechanism sequence-to-sequence sequence-to-sequence-models sockeye attention-is-all-you-need attention-alignment-visualization attention-model seq2seq convolutional-neural-networks translationMachineLearningSamples-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-learning deep-neural-networks natural-language-processing word-embeddings keras-neural-networks tensorflow-tutorials azure-machine-learningdeep_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.
anaconda docker azure machine-learning deep-learning deep-neural-networksdbn-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.
deep-belief-network dbn dbn-cuda deep-learning deep-neural-networks neural-networksDeepLearning.scala-website - The website of DeepLearning.scala
The website of DeepLearning.scala
deeplearning neural-network deep-neural-networks deep-learning-tutorialDeeplearning4JOnAndroid - Deeplearning4J in Android apps
Sample Android Studio project that shows you how to use Deeplearning4J in Android apps.
deep-learning deep-neural-networks androidtfmesos - Tensorflow in Docker on Mesos #tfmesos #tensorflow #mesos
TFMesos is a lightweight framework to help running distributed Tensorflow Machine Learning tasks on Apache Mesos within Docker and Nvidia-Docker . TFMesos dynamically allocates resources from a Mesos cluster, builds a distributed training cluster for Tensorflow, and makes different training tasks mangeed and isolated in the shared Mesos cluster with the help of Docker.
tensorflow mesos nvidia-docker machine-learning distributed deep-learning deep-neural-networks ml neural-network dockerDeepNetworks - My implementations of deep neural networks for practice.
My implementations of deep neural networks for practice.
deep-learning deep-neural-networks generative-adversarial-network mnist jupyter-notebook wasserstein-gan dragan discoganSocial Icons
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