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https://github.com/u2takey/tensorflow-in-depth

tensorflow in depth
https://github.com/u2takey/tensorflow-in-depth

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tensorflow in depth

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# tensorflow-in-depth



# tensorflow-in-depth

Unlike [awesome-tensorflow](https://github.com/jtoy/awesome-tensorflow), [tensorflow-in-depth](https://github.com/u2takey/tensorflow-in-depth) collects tutorials, projects, papers, blogs focusing on underlying mechanism of `tensorflow` and similar machine/deep learnning framework.



## Table of Contents



- [Tutorials](#github-tutorials)

- [Models/Projects](#github-projects)

- [Videos/Course](#video)

- [Papers](#papers)

- [Blog/Slides](#blogs)

- [Community](#community)

- [Books](#books)






## Tutorials



* [TensorLayer](http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html) - Modular implementation for TensorFlow's official tutorials. ([CN](https://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html)).

* [Tensorflow Architecture](https://www.tensorflow.org/extend/architecture) This document is for developers who want to extend TensorFlow in some way not supported by current APIs, hardware engineers who want to optimize for TensorFlow, implementers of machine learning systems working on scaling and distribution, or anyone who wants to look under Tensorflow's hood.

* [MPI Tutorial](http://mpitutorial.com/) A website dedicated to providing useful tutorials about the Message Passing Interface (MPI).






## Models/Projects



* [TensorFlow White Paper Notes](https://github.com/samjabrahams/tensorflow-white-paper-notes) - Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation

* [Deep-Q learning Pong with TensorFlow and PyGame](http://www.danielslater.net/2016/03/deep-q-learning-pong-with-tensorflow.html)

* [GoogleNet Convolutional Neural Network Groups Movie Scenes By Setting](https://github.com/agermanidis/thingscoop) - Search, filter, and describe videos based on objects, places, and other things that appear in them

* [Neural machine translation between the writings of Shakespeare and modern English using TensorFlow](https://github.com/tokestermw/tensorflow-shakespeare) - This performs a monolingual translation, going from modern English to Shakespeare and vice-versa.






## Videos/Course

* [TensorFlow on YouTube](https://www.youtube.com/tensorflow) - TensorFlow's Page On Youtube, [TensorFlow Dev Summit 2018](https://www.youtube.com/playlist?list=PLQY2H8rRoyvxjVx3zfw4vA4cvlKogyLNN)，[Coding TensorFlow](https://www.youtube.com/playlist?list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx).

* [Designing and Building Applications for Extreme Scale Systems](http://wgropp.cs.illinois.edu/courses/cs598-s16/) - Learn how to design and implement applications for extreme scale systems, including analyzing and understanding the performance of applications, the primary causes of poor performance and scalability, and how both the choice of algorithm and programming system impact achievable performance.The course covers multi-and many-core processors, interconnects in HPC systems, parallel I/O, and the impact of faults on program and algorithm design.






## Papers



* [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](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

* [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

* [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)

* [Scaling Distributed Machine Learning with the Parameter Server](https://www.cs.cmu.edu/~dga/papers/osdi14-paper-li_mu.pdf) - How parameter server works, proposed a parameter server framework for distributed machine learning problems.






## Blog/Slides



* [TensorFlow - Not Just For Deep Learning](http://terrytangyuan.github.io/2016/08/06/tensorflow-not-just-deep-learning/) From [YuanTang's Blog](https://terrytangyuan.github.io/), YuanTang is committer of tensorflow、MXNet、XGBoost.

* [How Does The TensorFlow Work](https://www.letslearnai.com/2018/02/02/how-does-the-machine-learning-library-tensorflow-work.html) How TensorFlow Work

* [A tour through the TensorFlow codebase](http://public.kevinrobinsonblog.com/docs/A%20tour%20through%20the%20TensorFlow%20codebase%20-%20v4.pdf) TensorFlow code tour.

* [An Introduction to TensorFlow architecture](https://www.slideshare.net/ManiGoswami/into-to-tensorflow-architecture-v2) - An Introduction to TensorFlow architecture and underlying mechanism.

* [Autodiff Workshop](https://autodiff-workshop.github.io/) - The future of gradient-based machine learning software and techniques, NIPS 2017.






## Community



* [Stack Overflow](http://stackoverflow.com/questions/tagged/tensorflow)

* [@TensorFlow on Twitter](https://twitter.com/tensorflow)

* [Reddit](https://www.reddit.com/r/tensorflow)

* [Mailing List](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss)






## Books