tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

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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

tf_tutorial_plus - Tutorials for TensorFlow APIs the official documentation doesn't cover

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Tutorials for TensorFlow APIs the official documentation doesn't cover

tensorflow tensorflow-tutorials tensorflow-examples tf-tutorial tensorflow-exercises seq2seq

TensorFlow-Book - Accompanying source code for Machine Learning with TensorFlow

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This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

tensorflow machine-learning regression convolutional-neural-networks logistic-regression book reinforcement-learning autoencoder linear-regression classification clustering

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

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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

Distributed-TensorFlow-Guide - Distributed TensorFlow basics and examples of training algorithms

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This guide is a collection of distributed training examples (that can act as boilerplate code) and a tutorial of basic distributed TensorFlow. Many of the examples focus on implementing well-known distributed training schemes, such as those available in dist-keras which were discussed in the author's blog post. Almost all the examples can be run on a single machine with a CPU, and all the examples only use data-parallelism (i.e., between-graph replication).

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

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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

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

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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

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

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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

Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

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In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

tensorflow tensorflow-tutorials gan generative-adversarial-network rnn cnn classification regression autoencoder deep-q-network dqn machine-learning tutorial dropout neural-network

TensorFlow-Machine-Learning-Cookbook - Code repository for TensorFlow Machine Learning Cookbook by Packt

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This is the code repository for TensorFlow Machine Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.

stanford-tensorflow-tutorials - This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research

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This repository contains code examples for the course CS 20: TensorFlow for Deep Learning Research. It will be updated as the class progresses. Detailed syllabus and lecture notes can be found here. For this course, I use python3.6 and TensorFlow 1.4.1. For setup instruction and the list of dependencies, please see the setup folder of this repository.

tensorflow deep-learning tutorial nlp natural-language-processing chatbot machine-learning stanford course-materials

tensorflow-tutorial - TensorFlow and Deep Learning Tutorials

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Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnn

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

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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

tensorlayer-tricks - How to use TensorLayer

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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-experiments

TensorFlowOnSpark - TensorFlowOnSpark brings TensorFlow programs onto Apache Spark clusters

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TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from deep learning framework TensorFlow and big-data frameworks Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers.TensorFlowOnSpark was developed by Yahoo for large-scale distributed deep learning on our Hadoop clusters in Yahoo's private cloud.

tensorflow spark yahoo machine-learning cluster featured

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-lstm-regression - Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

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The objective is to predict continuous values, sin and cos functions in this example, based on previous observations using the LSTM architecture. This example has been updated with a new version compatible with the tensrflow-1.1.0. This new version is using a library polyaxon that provides an API to create deep learning models and experiments based on tensorflow.

lstm tensorflow recurrent-networks deep-learning sequence-prediction tensorflow-lstm-regression jupyter time-series recurrent-neural-networks

probability - Probabilistic reasoning and statistical analysis in TensorFlow

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TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation. Our probabilistic machine learning tools are structured as follows.

tensorflow bayesian-methods deep-learning machine-learning data-science neural-networks statistics probabilistic-programming

TensorFlow-World - :earth_americas: Simple and ready-to-use tutorials for TensorFlow

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This repository aims to provide simple and ready-to-use tutorials for TensorFlow. The explanations are present in the wiki associated with this repository. Each tutorial includes source code and associated documentation.

deep-learning neural-network tensorflow machine-learning computer-vision

Deep_reinforcement_learning_Course - Implementations from the free course Deep Reinforcement Learning with Tensorflow

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Deep Reinforcement Learning Course is a free series of blog posts and videos 🆕 about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow. 📜The articles explain the concept from the big picture to the mathematical details behind it.

deep-reinforcement-learning qlearning deep-learning tensorflow-tutorials tensorflow ppo a2c actor-critic deep-q-network deep-q-learning