Awesome TensorFlow  텐서플로우 예제와 활용예들

A curated list of awesome TensorFlow experiments, libraries, and projects. Inspired by awesome-machine-learning.

What is TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. In other words, the best way to build deep learning models.

More info here.

Table of Contents

Tutorials

Models/Projects

Powered by TensorFlow

  • YOLO TensorFlow - Implementation of 'YOLO : Real-Time Object Detection'
  • android-yolo - Real-time object detection on Android using the YOLO network, powered by TensorFlow.
  • Magenta - Research project to advance the state of the art in machine intelligence for music and art generation

Libraries

  • Lattice - Implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow
  • tf.contrib.learn - Simplified interface for Deep/Machine Learning (now part of TensorFlow)
  • tensorflow.rb - TensorFlow native interface for ruby using SWIG
  • tflearn - Deep learning library featuring a higher-level API
  • TensorLayer - Deep learning and reinforcement learning library for researchers and engineers
  • TensorFlow-Slim - High-level library for defining models
  • TensorFrames - TensorFlow binding for Apache Spark
  • TensorForce - TensorForce: A TensorFlow library for applied reinforcement learning
  • TensorFlowOnSpark - initiative from Yahoo! to enable distributed TensorFlow with Apache Spark.
  • caffe-tensorflow - Convert Caffe models to TensorFlow format
  • keras - Minimal, modular deep learning library for TensorFlow and Theano
  • SyntaxNet: Neural Models of Syntax - A TensorFlow implementation of the models described in Globally Normalized Transition-Based Neural Networks, Andor et al. (2016)
  • keras-js - Run Keras models (tensorflow backend) in the browser, with GPU support
  • NNFlow - Simple framework allowing to read-in ROOT NTuples by converting them to a Numpy array and then use them in Google Tensorflow.
  • Sonnet - Sonnet is DeepMind's library built on top of TensorFlow for building complex neural networks.
  • tensorpack - Neural Network Toolbox on TensorFlow focusing on training speed and on large datasets.

Videos

Papers

경축! 아무것도 안하여 에스천사게임즈가 새로운 모습으로 재오픈 하였습니다.
어린이용이며, 설치가 필요없는 브라우저 게임입니다.
https://s1004games.com

Official announcements

Blog posts

Community

Books

  • Machine Learning with TensorFlow by Nishant Shukla, computer vision researcher at UCLA and author of Haskell Data Analysis Cookbook. This book makes the math-heavy topic of ML approachable and practicle to a newcomer.
  • First Contact with TensorFlow by Jordi Torres, professor at UPC Barcelona Tech and a research manager and senior advisor at Barcelona Supercomputing Center
  • Deep Learning with Python - Develop Deep Learning Models on Theano and TensorFlow Using Keras by Jason Brownlee
  • TensorFlow for Machine Intelligence - Complete guide to use TensorFlow from the basics of graph computing, to deep learning models to using it in production environments - Bleeding Edge Press
  • Getting Started with TensorFlow - Get up and running with the latest numerical computing library by Google and dive deeper into your data, by Giancarlo Zaccone
  • Hands-On Machine Learning with Scikit-Learn and TensorFlow – by Aurélien Geron, former lead of the YouTube video classification team. Covers ML fundamentals, training and deploying deep nets across multiple servers and GPUs using TensorFlow, the latest CNN, RNN and Autoencoder architectures, and Reinforcement Learning (Deep Q).
  • Building Machine Learning Projects with Tensorflow – by Rodolfo Bonnin. This book covers various projects in TensorFlow that expose what can be done with TensorFlow in different scenarios. The book provides projects on training models, machine learning, deep learning, and working with various neural networks. Each project is an engaging and insightful exercise that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors.
  • Deep Learning using TensorLayer - by Hao Dong et al. This book covers both deep learning and the implmentation by using TensorFlow and TensorLayer.

Contributions

Your contributions are always welcome!

If you want to contribute to this list (please do), send me a pull request or contact me @jtoy Also, if you notice that any of the above listed repositories should be deprecated, due to any of the following reasons:

  • Repository's owner explicitly say that "this library is not maintained".
  • Not committed for long time (2~3 years).

More info on the guidelines

 

[출처] https://github.com/jtoy/awesome-tensorflow

 

 

 

본 웹사이트는 광고를 포함하고 있습니다.
광고 클릭에서 발생하는 수익금은 모두 웹사이트 서버의 유지 및 관리, 그리고 기술 콘텐츠 향상을 위해 쓰여집니다.
번호 제목 글쓴이 날짜 조회 수
공지 오라클 기본 샘플 데이터베이스 졸리운_곰 2014.01.02 86421
공지 [SQL컨셉] 서적 "SQL컨셉"의 샘플 데이타 베이스 SAMPLE DATABASE of ORACLE 가을의 곰을... 2013.02.10 78855
공지 [G_SQL] Sample Database 가을의 곰을... 2012.05.20 95619
15 [hadoop] Cloudera Quick Start VM in Hyper-V file 졸리운_곰 2022.11.14 3818
14 [hadoop][java][MapReduce] 맵리듀스 원리와 그 과정 졸리운_곰 2021.02.28 1659
13 [hadoop][mapreduce][csv file] [Reference] : Hadoop MapReduce Join & Counter with Example file 졸리운_곰 2021.02.23 1536
12 [hadoop][mapreduce][csv file] Hadoop & Mapreduce Examples: Create First Program in Java file 졸리운_곰 2021.02.23 1856
11 하둡 맵리듀스(MapReduce) 알아보자 file 졸리운_곰 2019.04.22 2559
10 [하둡] 맵리듀스(MapReduce) 이해하기 file 졸리운_곰 2019.04.22 1982
9 맵/리듀스 (Map/Reduce) 이해하기 file 졸리운_곰 2019.04.22 2650
8 아파치 하둡 HDFS 사용법(Cloudera 사용) file 졸리운_곰 2017.04.19 1599
7 클라우데라 배포판을 이용한 하둡 및 빅데이터 오픈소스 설치하기 file 졸리운_곰 2017.02.14 2027
6 빅데이터 플랫폼 아키텍처의 두 가지 file 졸리운_곰 2016.05.29 1685
5 Hadoop 적용 사례 기사 정리 졸리운_곰 2016.05.29 2002
4 빅데이터 잡설 , 하둡은 어디로 갈꺼나 … file 졸리운_곰 2016.05.29 1741
3 하둡, 데이터 분석에 활용하기까지 file 졸리운_곰 2016.05.29 2139
2 A Guide to Python Frameworks for Hadoop file 졸리운_곰 2016.04.30 3074
1 Writing an Hadoop MapReduce Program in Python file 졸리운_곰 2016.04.30 2190
대표 김성준 주소 : 경기 용인 분당수지 U타워 등록번호 : 142-07-27414
통신판매업 신고 : 제2012-용인수지-0185호 출판업 신고 : 수지구청 제 123호 개인정보보호최고책임자 : 김성준 sjkim70@stechstar.com
대표전화 : 010-4589-2193 [fax] 02-6280-1294 COPYRIGHT(C) stechstar.com ALL RIGHTS RESERVED