update lists

terminal/tensorflow

@@ -1,4 +1,4 @@
-                                   Awesome TensorFlow  !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/jtoy/awesome)
+                                        Awesome TensorFlow  !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/jtoy/awesome)
 
 A curated list of awesome TensorFlow experiments, libraries, and projects. Inspired by awesome-machine-learning.
 
@@ -41,8 +41,7 @@
 ⟡ Classification on time series (https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition) - Recurrent Neural Network classification in TensorFlow with LSTM on cellphone sensor data
 ⟡ Getting Started with TensorFlow on Android (https://omid.al/posts/2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html) - Build your first TensorFlow Android app
 ⟡ Predict time series (https://github.com/guillaume-chevalier/seq2seq-signal-prediction) - Learn to use a seq2seq model on simple datasets as an introduction to the vast array of possibilities that this architecture offers
-⟡ Single Image Random Dot Stereograms
- (https://github.com/Mazecreator/TensorFlow-SIRDS) - SIRDS is a means to present 3D data in a 2D image. It allows for scientific data display of a waterfall type plot with no hidden lines due to perspective.
+⟡ Single Image Random Dot Stereograms (https://github.com/Mazecreator/TensorFlow-SIRDS) - SIRDS is a means to present 3D data in a 2D image. It allows for scientific data display of a waterfall type plot with no hidden lines due to perspective.
 ⟡ CS20 SI: TensorFlow for DeepLearning Research (http://web.stanford.edu/class/cs20si/syllabus.html) - Stanford Course about Tensorflow from 2017 - Syllabus (http://web.stanford.edu/class/cs20si/syllabus.html) - Unofficial Videos 
 (https://youtu.be/g-EvyKpZjmQ?list=PLSPPwKHXGS2110rEaNH7amFGmaD5hsObs)
 ⟡ TensorFlow World (https://github.com/astorfi/TensorFlow-World) - Concise and ready-to-use TensorFlow tutorials with detailed documentation are provided.
@@ -107,8 +106,8 @@
 (http://ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf)
 ⟡ CNN visualization using Tensorflow (https://github.com/InFoCusp/tf_cnnvis) - Tensorflow implementation of "Visualizing and Understanding Convolutional Networks" (https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf)
 ⟡ VGAN Tensorflow (https://github.com/Singularity42/VGAN-Tensorflow) - Tensorflow implementation for MIT "Generating Videos with Scene Dynamics" (http://carlvondrick.com/tinyvideo/) by Vondrick et al.
-⟡ 3D Convolutional Neural Networks in TensorFlow (https://github.com/astorfi/3D-convolutional-speaker-recognition) - Implementation of "3D Convolutional Neural Networks for Speaker Verification application" 
-(https://arxiv.org/abs/1705.09422) in TensorFlow by Torfi et al.
+⟡ 3D Convolutional Neural Networks in TensorFlow (https://github.com/astorfi/3D-convolutional-speaker-recognition) - Implementation of "3D Convolutional Neural Networks for Speaker Verification application" (https://arxiv.org/abs/1705.09422) in 
+TensorFlow by Torfi et al.
 ⟡ U-Net (https://github.com/zsdonghao/u-net-brain-tumor) - For Brain Tumor Segmentation
 ⟡ Spatial Transformer Networks (https://github.com/zsdonghao/Spatial-Transformer-Nets) - Learn the Transformation Function 
 ⟡ Lip Reading - Cross Audio-Visual Recognition using 3D Architectures in TensorFlow (https://github.com/astorfi/lip-reading-deeplearning) - TensorFlow Implementation of "Cross Audio-Visual Recognition in the Wild Using Deep Learning" 
@@ -122,10 +121,10 @@
 ⟡ TensorNets (https://github.com/taehoonlee/tensornets) - 40+ Popular Computer Vision Models With Pre-trained Weights.
 ⟡ Ladder Network (https://github.com/divamgupta/ladder_network_keras) - Implementation of Ladder Network for Semi-Supervised Learning in Keras and Tensorflow
 ⟡ TF-Unet (https://github.com/juniorxsound/TF-Unet) - General purpose U-Network implemented in Keras for image segmentation
-⟡ Sarus TF2 Models (https://github.com/sarus-tech/tf2-published-models) - A long list of recent generative models implemented in clean, easy to reuse, Tensorflow 2 code (Plain Autoencoder, VAE, VQ-VAE, PixelCNN, Gated PixelCNN, 
-PixelCNN++, PixelSNAIL, Conditional Neural Processes).
-⟡ Model Maker (https://www.tensorflow.org/lite/guide/model_maker) - A transfer learning library that simplifies the process of training, evaluation and deployment for TensorFlow Lite models (support: Image Classification, Object 
-Detection, Text Classification, BERT Question Answer, Audio Classification, Recommendation etc.; API reference (https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker)).
+⟡ Sarus TF2 Models (https://github.com/sarus-tech/tf2-published-models) - A long list of recent generative models implemented in clean, easy to reuse, Tensorflow 2 code (Plain Autoencoder, VAE, VQ-VAE, PixelCNN, Gated PixelCNN, PixelCNN++, 
+PixelSNAIL, Conditional Neural Processes).
+⟡ Model Maker (https://www.tensorflow.org/lite/guide/model_maker) - A transfer learning library that simplifies the process of training, evaluation and deployment for TensorFlow Lite models (support: Image Classification, Object Detection, Text 
+Classification, BERT Question Answer, Audio Classification, Recommendation etc.; API reference (https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker)).
 
 
 
@@ -165,8 +164,9 @@
 ⟡ TensorIO (https://doc-ai.github.io/tensorio/) - Lightweight, cross-platform library for deploying TensorFlow Lite models to mobile devices. 
 ⟡ StellarGraph (https://github.com/stellargraph/stellargraph) - Machine Learning on Graphs, a Python library for machine learning on graph-structured (network-structured) data.
 ⟡ DeepBay (https://github.com/ElPapi42/DeepBay) - High-Level Keras Complement for implement common architectures stacks, served as easy to use plug-n-play modules
-⟡ Tensorflow-Probability (https://www.tensorflow.org/probability) - Probabalistic programming built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware.
+⟡ Tensorflow-Probability (https://www.tensorflow.org/probability) - Probabilistic programming built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware.
 ⟡ TensorLayerX (https://github.com/tensorlayer/TensorLayerX) - TensorLayerX: A Unified Deep Learning Framework for All Hardwares, Backends and OS, including TensorFlow.
+⟡ Txeo (https://github.com/rdabra/txeo) - A modern C++ wrapper for TensorFlow.
 
 
 
@@ -189,8 +189,7 @@
 ⟡ Why Google wants everyone to have access to TensorFlow (http://video.foxnews.com/v/4611174773001/why-google-wants-everyone-to-have-access-to-tensorflow/?#sp=show-clips)
 ⟡ Videos from TensorFlow Silicon Valley Meet Up 1/19/2016 (http://blog.altoros.com/videos-from-tensorflow-silicon-valley-meetup-january-19-2016.html)
 ⟡ Videos from TensorFlow Silicon Valley Meet Up 1/21/2016 (http://blog.altoros.com/videos-from-tensorflow-seattle-meetup-jan-21-2016.html)
-⟡ Stanford CS224d Lecture 7 - Introduction to TensorFlow, 19th Apr 2016
- (https://www.youtube.com/watch?v=L8Y2_Cq2X5s&index=7&list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam) - CS224d Deep Learning for Natural Language Processing by Richard Socher
+⟡ Stanford CS224d Lecture 7 - Introduction to TensorFlow, 19th Apr 2016 (https://www.youtube.com/watch?v=L8Y2_Cq2X5s&index=7&list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam) - CS224d Deep Learning for Natural Language Processing by Richard Socher
 ⟡ Diving into Machine Learning through TensorFlow (https://youtu.be/GZBIPwdGtkk?list=PLBkISg6QfSX9HL6us70IBs9slFciFFa4W) - Pycon 2016 Portland Oregon, Slide (https://storage.googleapis.com/amy-jo/talks/tf-workshop.pdf) & Code 
 (https://github.com/amygdala/tensorflow-workshop) by Julia Ferraioli, Amy Unruh, Eli Bixby
 ⟡ Large Scale Deep Learning with TensorFlow (https://youtu.be/XYwIDn00PAo) - Spark Summit 2016 Keynote by Jeff Dean
@@ -208,21 +207,21 @@
  (http://download.tensorflow.org/paper/whitepaper2015.pdf) - This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google
 ⟡ TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks (https://arxiv.org/pdf/1708.02637.pdf)
 ⟡ TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning (https://arxiv.org/abs/1612.04251)
-⟡ Comparative Study of Deep Learning Software Frameworks (http://arxiv.org/abs/1511.06435) - The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on
-a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings
+⟡ Comparative Study of Deep Learning Software Frameworks (http://arxiv.org/abs/1511.06435) - The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single 
+machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings
 ⟡ Distributed TensorFlow with MPI (http://arxiv.org/abs/1603.02339) - In this paper, we extend recently proposed Google TensorFlow for execution on large scale clusters using Message Passing Interface (MPI)
 ⟡ Globally Normalized Transition-Based Neural Networks (http://arxiv.org/abs/1603.06042) - This paper describes the models behind SyntaxNet (https://github.com/tensorflow/models/tree/master/syntaxnet).
 ⟡ TensorFlow: A system for large-scale machine learning (https://arxiv.org/abs/1605.08695) - This paper describes the TensorFlow dataflow model in contrast to existing systems and demonstrate the compelling performance
-⟡ TensorLayer: A Versatile Library for Efficient Deep Learning Development (https://arxiv.org/abs/1707.08551) - This paper describes a versatile Python library that aims at helping researchers and engineers efficiently develop deep 
-learning systems. (Winner of The Best Open Source Software Award of ACM MM 2017)
+⟡ TensorLayer: A Versatile Library for Efficient Deep Learning Development
+ (https://arxiv.org/abs/1707.08551) - This paper describes a versatile Python library that aims at helping researchers and engineers efficiently develop deep learning systems. (Winner of The Best Open Source Software Award of ACM MM 2017)
 
 
 
 Official announcements
 
 ⟡ TensorFlow: smarter machine learning, for everyone (https://googleblog.blogspot.com/2015/11/tensorflow-smarter-machine-learning-for.html) - An introduction to TensorFlow
-⟡ Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source (http://googleresearch.blogspot.com/2016/05/announcing-syntaxnet-worlds-most.html) - Release of SyntaxNet, "an open-source neural network framework implemented in
-TensorFlow that provides a foundation for Natural Language Understanding systems.
+⟡ Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source
+ (http://googleresearch.blogspot.com/2016/05/announcing-syntaxnet-worlds-most.html) - Release of SyntaxNet, "an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding systems.
 
 Blog posts
 ⟡ Official Tensorflow Blog (http://blog.tensorflow.org/)
@@ -238,8 +237,8 @@
 ⟡ Using TensorBoard to Visualize Image Classification Retraining in TensorFlow (http://maxmelnick.com/2016/07/04/visualizing-tensorflow-retrain.html)
 ⟡ TFRecords Guide (http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/) semantic segmentation and handling the TFRecord file format.
 ⟡ TensorFlow Android Guide (https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc) - Android TensorFlow Machine Learning Example.
-⟡ TensorFlow Optimizations on Modern Intel® Architecture (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture) - Introduces TensorFlow optimizations on Intel® Xeon® and Intel® Xeon Phi™ 
-processor-based platforms based on an Intel/Google collaboration.
+⟡ TensorFlow Optimizations on Modern Intel® Architecture
+ (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture) - Introduces TensorFlow optimizations on Intel® Xeon® and Intel® Xeon Phi™ processor-based platforms based on an Intel/Google collaboration.
 ⟡ Coca-Cola's Image Recognition App (https://developers.googleblog.com/2017/09/how-machine-learning-with-tensorflow.html) Coca-Cola's product code image recognizing neural network with user input feedback loop.
 ⟡ How Does The TensorFlow Work (https://www.letslearnai.com/2018/02/02/how-does-the-machine-learning-library-tensorflow-work.html) How Does The Machine Learning Library TensorFlow Work?
 
@@ -266,16 +265,16 @@
  (https://bleedingedgepress.com/tensor-flow-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
  (https://www.packtpub.com/big-data-and-business-intelligence/getting-started-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 (http://shop.oreilly.com/product/0636920052289.do) – 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 (https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-projects-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 (http://www.broadview.com.cn/book/5059) - by Hao Dong et al. This book covers both deep learning and the implmentation by using TensorFlow and TensorLayer.
-⟡ TensorFlow 2.0 in Action (https://www.manning.com/books/tensorflow-in-action) - by Thushan Ganegedara. This practical guide to building deep learning models with the new features of TensorFlow 2.0 is filled with engaging projects, 
-simple language, and coverage of the latest algorithms.
-⟡ Probabilistic Programming and Bayesian Methods for Hackers (https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - by Cameron Davidson-Pilon. Introduction to Bayesian methods and 
-probabalistic graphical models using tensorflow-probability (and, alternatively PyMC2/3). 
+⟡ Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://shop.oreilly.com/product/0636920052289.do) – 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 (https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-projects-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 (http://www.broadview.com.cn/book/5059) - by Hao Dong et al. This book covers both deep learning and the implementation by using TensorFlow and TensorLayer.
+⟡ TensorFlow 2.0 in Action (https://www.manning.com/books/tensorflow-in-action) - by Thushan Ganegedara. This practical guide to building deep learning models with the new features of TensorFlow 2.0 is filled with engaging projects, simple 
+language, and coverage of the latest algorithms.
+⟡ Probabilistic Programming and Bayesian Methods for Hackers (https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - by Cameron Davidson-Pilon. Introduction to Bayesian methods and probabilistic 
+graphical models using tensorflow-probability (and, alternatively PyMC2/3). 
 
 
 
@@ -301,3 +300,5 @@
 ⟡ Some of the python libraries were cut-and-pasted from vinta (https://github.com/vinta/awesome-python)
 ⟡ The few go reference I found where pulled from this page (https://code.google.com/p/go-wiki/wiki/Projects#Machine_Learning)
 
+
+tensorflow Github: https://github.com/jtoy/awesome-tensorflow