frugally-deep - Header-only library for using Keras models in C++.

•    C++

---

Would you like to build/train a model using Keras/Python? And would you like run the prediction (forward pass) on your model in C++ without linking your application against TensorFlow? Then frugally-deep is exactly for you. Layer types typically used in image recognition/generation are supported, making many popular model architectures possible (see Performance section).

tensorflow deep-learning keras cpp cpp14 header-only library c-plus-plus c-plus-plus-14 convolutional-neural-networks prediction machine-learning

awesome-tensorflow-lite - TensorFlow Lite models, samples, tutorials, tools and learning resources.

•    

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TensorFlow Lite is a set of tools that help convert and optimize TensorFlow models to run on mobile and edge devices. It's currently running on more than 4 billion devices! With TensorFlow 2.x, you can train a model with tf.Keras, easily convert a model to .tflite and deploy it; or you can download a pretrained TensorFlow Lite model from the model zoo. Please submit a PR if you would like to contribute and follow the guidelines here.

android ios awesome mobile computer-vision deep-learning model-zoo tensorflow sample-app awesome-list keras-tutorials flutter tensorflow-models mlkit tensorflow-lite tflite tfhub tensorflow-keras mediapipe tflite-models

AndroidTensorFlowMachineLearningExample - Android TensorFlow MachineLearning Example (Building TensorFlow for Android)

•    Java

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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-tutorial

recommenders-addons - Additional utils and helpers to extend TensorFlow when build recommendation systems, contributed and maintained by SIG Recommenders

•    Cuda

---

TensorFlow Recommenders Addons(TFRA) are a collection of projects related to large-scale recommendation systems built upon TensorFlow by introducing the Dynamic Embedding Technology to TensorFlow that make TensorFlow more suitable for trainning models of Search, Recommendations and Advertising. These projects are contributed and maintained by the community. Those contributions will be complementary to TensorFlow Core and TensorFlow Recommenders etc. See approved TensorFlow RFC #313.

tensorflow-recommenders-addons sig-recommenders

tensorflow-windows-wheel - Tensorflow prebuilt binary for Windows

•    Python

---

This repo contains all you need that work with tensorflow on windows.

library build cpp binary tensorflow wheel

Tensorflow-Project-Template - A best practice for tensorflow project template architecture.

•    Python

---

A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributing in tensorflow projects here's a tensorflow project template that combines simplcity, best practice for folder structure and good OOP design. The main idea is that there's much stuff you do every time you start your tensorflow project, so wrapping all this shared stuff will help you to change just the core idea every time you start a new tensorflow project. You will find a template file and a simple example in the model and trainer folder that shows you how to try your first model simply.

tesnorflow software-engineering oop deep-learning neural-network convolutional-neural-networks tensorflow-tutorials deep-learning-tutorial best-practices tensorflow template

tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

•    Jupyter

---

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

tensorflow tensorflow-cookbook linear-regression neural-network tensorflow-algorithms rnn cnn svm nlp packtpub machine-learning tensorboard classification regression kmeans-clustering genetic-algorithm ode

EffectiveTensorflow - TensorFlow tutorials and best practices.

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We aim to gradually expand this series by adding new articles and keep the content up to date with the latest releases of TensorFlow API. If you have suggestions on how to improve this series or find the explanations ambiguous, feel free to create an issue, send patches, or reach out by email. The most striking difference between TensorFlow and other numerical computation libraries such as NumPy is that operations in TensorFlow are symbolic. This is a powerful concept that allows TensorFlow to do all sort of things (e.g. automatic differentiation) that are not possible with imperative libraries such as NumPy. But it also comes at the cost of making it harder to grasp. Our attempt here is to demystify TensorFlow and provide some guidelines and best practices for more effective use of TensorFlow.

tensorflow neural-network deep-learning machine-learning ebook

tensorflow-exercises - TensorFlow Exercises - focusing on the comparison with NumPy.

•    Python

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TensorFlow is arugably the most popular deep learning library as of 2017. This is designed to help those who want to familiarize themselves with TensorFlow functions. Particulary, I focus on comparing TensorFlow functions with the equivalent functions in NumPy, the de facto standard numerical computation library. I hope this will help you get comfortable with TensorFlow quickly.

tensorflow tensorflow-exercises numpy

addons - Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

•    Python

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TensorFlow Addons is a repository of contributions that conform to well-established API patterns, but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community). The maintainers of TensorFlow Addons can be found in the CODEOWNERS file of the repo. This file is parsed and pull requests will automatically tag the owners using a bot. If you would like to maintain something, please feel free to submit a PR. We encourage multiple owners for all submodules.

machine-learning deep-learning neural-network tensorflow tensorflow-addons

node-tensorflow - Node.js + TensorFlow

•    Javascript

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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.

tensorflow node-tensorflow nodejs machine-learning deep-learning npm-package tf tensor ml ai neural-networks neuralnetworks deeplearning model numerical-computation google

tensorflow-nodejs - TensorFlow Node

•    Javascript

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TensorFlow Node.js provides idiomatic JavaScript language bindings and a high layer API for Node.js users. Notice: This project is still under active development and not guaranteed to have a stable API. This is especially true because the underlying TensorFlow C API has not yet been stabilized as well.

tensorflow tensorflow-node nodejs binding

io - Dataset, streaming, and file system extensions maintained by TensorFlow SIG-IO

•    C++

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TensorFlow I/O is a collection of file systems and file formats that are not available in TensorFlow's built-in support. A full list of supported file systems and file formats by TensorFlow I/O can be found here. In the above MNIST example, the URL's to access the dataset files are passed directly to the tfio.IODataset.from_mnist API call. This is due to the inherent support that tensorflow-io provides for HTTP/HTTPS file system, thus eliminating the need for downloading and saving datasets on a local directory.

streaming tensorflow filesystem dataset tensorflow-io

tensorflow - TensorFlow for R

•    R

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TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs. The tensorflow package provides access to the complete TensorFlow API from within R.

TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners with Latest APIs

•    Jupyter

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This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as layers, estimator, dataset, ...).

tensorflow tutorial examples deep-learning machine-learning

Android-TensorFlow-Lite-Example - Android TensorFlow Lite Machine Learning Example

•    Java

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Android TensorFlow Lite Machine Learning Example

tensorflow tensorflow-tutorials machine-learning tensorflow-lite tensorflow-examples deep-learning deep-neural-networks android-example machine-learning-algorithms tflite

AndroidTensorFlowMNISTExample - Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)

•    Java

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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-tutorial

tensorflow-on-arm - TensorFlow for Arm

•    Shell

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Inspired by the tensorflow-on-raspberry-pi. Tool for compile tensorflow for arm. Python wheels for TensorFlow are being officially supported. As well, this repository maintain up-to-date tensorflow wheels for raspberry pi.

odroid-c2 raspberry-pi tensorflow machine-learning

BMW-TensorFlow-Inference-API-GPU - This is a repository for an object detection inference API using the Tensorflow framework

•    Python

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This is a repository for an object detection inference API using the Tensorflow framework. This repo is based on Tensorflow Object Detection API.

api docker dockerfile deep-neural-networks computer-vision deep-learning neural-network tensorflow gpu rest-api docker-swarm inference nvidia object-detection tensorflow-framework tensorflow-models detection-inference-api

rust - Rust language bindings for TensorFlow

•    Rust

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TensorFlow Rust provides idiomatic Rust language bindings for TensorFlow. Notice: This project is still under active development and not guaranteed to have a stable API. This is especially true because the underlying TensorFlow C API has not yet been stabilized as well.

tensorflow machine-learning