We have collection of more than 1 Million open source products ranging from Enterprise product to small libraries in all platforms. We aggregate information from all open source repositories. Search and find the best for your needs. Check out projects section.
AndroidTensorFlowMachineLearningExample - Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
tensorflow tensorflow-tutorials tensorflow-android machine-learning machine-learning-android tensorflow-models tensorflow-examples deep-learning deep-neural-networks deeplearning deep-learning-tutorialAwesome-CoreML-Models - Largest list of models for Core ML (for iOS 11+)
We've put up the largest collection of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques. We've created a site with better visualization of the models CoreML.Store, and are working on more advance features. If you've converted a Core ML model, feel free to submit an issue.
coreml coreml-model apple machine-learning curated-list coreml-framework coreml-models coremltools awesome-list models model download awesome core-ml ml caffe caffemodel tensorflow-models ios ios11AndroidTensorFlowMNISTExample - Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)
All pull requests are welcome, make sure to follow the contribution guidelines when you submit pull request.
tensorflow tensorflow-tutorials mnist-classification mnist machine-learning android tensorflow-models machine-learning-android tensorflow-android tensorflow-model mnist-model deep-learning deep-neural-networks deeplearning deep-learning-tutorialsimple_tensorflow_serving - Generic and easy-to-use serving service for machine learning models
Simple TensorFlow Serving is the generic and easy-to-use serving service for machine learning models. Read more in https://stfs.readthedocs.io. Install the server with pip.
tensorflow-models savedmodel tensorflow serving client http machine-learning deep-learningtensorlayer-tricks - How to use TensorLayer
While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.
tensorlayer tensorflow deep-learning machine-learning data-science neural-network reinforcement-learning neural-networks tensorflow-tutorials tensorflow-models computer-vision tensorflow-framework tensorflow-library tflearn keras tensorboard nlp natural-language-processing lasagne tensorflow-experimentskemono_puyo - :cat: Take kemono pictures and lines up 3, then tanoshii
This is a toy application that made at the hackathon. It is inspired by following two concepts. At first I tried to implement the feature to control the falling objects but to see the animal faces is so fun and cute. Because of this, I dropped its implementation.
tensorflow tensorflow-models object-detection kemono-friendstensorflow-kubernetes-art-classification - Train a TensorFlow model on Kubernetes to recognize art culture based on the collection from the Metropolitan Museum of Art
Read this in other languages: 中国. In this Code Pattern, we will use Deep Learning to train an image classification model. The data comes from the art collection at the New York Metropolitan Museum of Art and the metadata from Google BigQuery. We will use the Inception model implemented in TensorFlow and we will run the training on a Kubernetes cluster. We will save the trained model and load it later to perform inference. To use the model, we provide as input a picture of a painting and the model will return the likely culture, for instance "Italian, Florence" art. The user can choose other attributes to classify the art collection, for instance author, time period, etc. Depending on the compute resources available, the user can choose the number of images to train, the number of classes to use, etc. In this Code Pattern, we will select a small set of images and a small number of classes to allow the training to complete within a reasonable amount of time. With a large dataset, the training may take days or weeks.
tensorflow-models image-classification deep-learning ibm-bluemix kubernetes-cluster ibmcodesteppy-toolkit - Curated set of transformers that make your work with steppy faster and more effective :telescope:
Set of tools to make your work with Steppy faster and more effective. Steppy is a lightweight, open-source, Python library for fast and reproducible experimentation.
pipeline data-science machine-learning deep-learning steps nlp reproducible-research reproducibility steppy steppy-toolkit python3 keras tensorflow tensorflow-models keras-models pytorch pytorch-models open-source pipeline-frameworkTFaaS - TensorFlow as a Service, a general purpose framework to serve TF models.
A general purpose framework (written in Go) to serve TensorFlow models. It provides reach and flexible set of APIs to efficiently access your favorite TF models via HTTP interface. The TFaaS supports JSON and ProtoBuffer data-formats. Fore more information please visit curl client page.
tensorflow tf-model inference machine-learning deeplearning tensorflow-models prediction tfaas-servertf-bridge - Tensor Bridge: OpenAPI spec and REST wrapper around TensorFlow Serving
Tensor Bridge is an OpenAPI Specification as well as a simple Connexion wrapper for TensorFlow Serving. The specification was obtained by compiling an annotated tensor_bridge.proto using grpc-gateway. The result is located in swagger/tensor_bridge.json.
tensorflow-serving tensorflow tensorflow-models machine-learning machine-learning-apiGAN-Project-2018 - GAN in Tensorflow to be run via Linux command line
If you are sure all the libraries in requirements.txt are installed, go to $ python main.py ...
dcgan dcgan-tensorflow dcgan-mnist-tutorial tensorflow tensorflow-models linux-kernel generative-adversarial-network gan generative-adversarial-networkstensorflow-convolution-models
This repo also contains a notebook that shows the result of the different steps in the convolutional architectures.
tensorflow convolutional-models convolution cnn cnn-model cnn-architecture tensorflow-modelsMango - Question Answering NLP w/ character-level RNN w/ TensorFlow! ℳ🖊 (QRN and EntNet)
The different models will first be challenged with the bAbI dataset from FAIR and the SQuAD dataset from Stanford. EntNet: very soon ...
deep-learning machine-learning qrn entnet tensorflow rnn rnn-tensorflow tensorflow-experiments tensorflow-modelscramer-gan - Tensorflow Implementation on "The Cramer Distance as a Solution to Biased Wasserstein Gradients" (https://arxiv
Tensorflow Implementation on "The Cramer Distance as a Solution to Biased Wasserstein Gradients" (https://arxiv.org/pdf/1705.10743.pdf)
tensorflow generative-adversarial-network generative-model generative tensorflow-experiments tensorflow-models wasserstein-gan cramer-ganfast-weights - Implementation of the paper [Using Fast Weights to Attend to the Recent Past](https://arxiv
This script generates a file called associative-retrieval.pkl, which can be used for training. The following is the accuracy and loss graph for R=20. The experiments are barely tuned.
fast-weights accuracy tensorflow tensorflow-models deep-learningwgan - Tensorflow Implementation of Wasserstein GAN (and Improved version in wgan_v2)
Tensorflow implementation of Wasserstein GAN.
tensorflow generative generative-adversarial-network generative-model tensorflow-models tensorflow-experimentstensorflow_template - A simple template for tensorflow project. A high-level version:
A deep learning template for tensorflow, of which the idea is from another project MrGemy95/Tensorflow-Project-Template. All you need to do is finish the following four functions.
tensorflow tensorflow-template tensorflow-models tensorflow-tutorials neural-network
We have large collection of open source products. Follow the tags from
Tag Cloud >>
Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
Add Projects.
