读《TensorFlow技术解析与实战》知识点总结

趁十一国庆时间充沛、可以系统给自己充充电了，认真阅读了李佳璇的《TensorFlow技术解析与实战》，该书以TensorFlow为核心，从环境的准备，到PlayGroud/TensorBoard可视化面板的展现；从编程模型、常用API的初探，到一个一个案例的实战；从神经元函数及游湖方法，到升级网络的发展及TensorFlow实现做了一一的归纳和总结，最后以MNIST数据集的应用、人脸识别的应用、自然语言处理应用、对抗网络的应用、Debugger/Kubernetes/OnSpark使用、以及训练模型在IOS/Android实战，从广度来说，对初学者来说是一本很好的读物。

知识点1：论文和实践结合学习深度学习的方法

知识点2：TensorFlow系统架构

知识点3：TensorFlow编程模型

参考地址：https://www.tensorflow.org/images/tensors_flowing.gif

知识点4：中国科学院计算技术研究所刘昕博士整理的卷积神经网络结构演化的历史

知识点5：循环神经网络发展史

知识点6：TensorFlowOnSpark系统架构图

知识点7：参考资料

参考网址（Blue）：

http://yahoohadoop.tumblr.com/post/157196317141/open-sourcing-tensorflowonspark-distributed-deep

Models built with TensorFlow(其中包含official models/research models/tutorial models):

https://github.com/tensorflow/models/tree/master/

The Unreasonable Effectiveness of Recurrent Neural Networks：

http://karpathy.github.io/2015/05/21/rnn-effectiveness/

TensorFlow官网：

https://www.tensorflow.org/

Ipython官网：

http://ipython.org/notebook.html

Deep Leaning with Dynamic Computation Graphs

https://openreview.net/pdf?id=ryrGawqex

Tree-Structured Long Short-Term Memory Networks：

https://github.com/stanfordnlp/treelstm

ImageNet Classification with Deep Convolutional Neural Networks：

http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification：

https://arxiv.org/abs/1502.01852

Deep Residual Learning for Image Recognition：https://arxiv.org/abs/1512.03385

Kaggle官网：https://www.kaggle.com/

天池大数据比赛官网：https://tianchi.aliyun.com/

Yi产品：http://www.dress-plus.com/product_page1.html

Face++旷视：https://www.faceplusplus.com.cn/

讯飞开放平台：http://www.xfyun.cn/

地平线：http://www.horizon.ai/

TensorFlow GitHub：https://github.com/tensorflow/tensorflow/

PyPI - the Python Package Index：https://pypi.python.org/pypi

Image processing in Python：http://scikit-image.org/

Natural Language Toolkit：http://www.nltk.org/

Tinker With a Neural Network Righ：http://playground.tensorflow.org

Data Compression Programs：http://mattmahoney.net/dc/

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

http://download.tensorflow.org/paper/whitepaper2015.pdf

基于java语言的API(package tensorflow):

https://www.tensorflow.org/api_docs/java/reference/org/tensorflow/package-summary

基于Go语言的API（package tensorflow）：

https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

https://arxiv.org/abs/1502.03167

Neural Networks for Machine Learning(Coursera课程)

https://www.coursera.org/learn/neural-networks

An overview of gradient descent optimization algorithms(blue):

http://sebastianruder.com/optimizing-gradient-descent/

Tornado Web Server: http://www.tornadoweb.org/en/stable/

Notes on Convolutional Neural Networks:

http://cogprints.org/5869/1/cnn_tutorial.pdf

优达学城（UDACITY）_视频课程网：https://cn.udacity.com/courses/all

机器学习到深度学习系统系列课程（视频+项目）：

https://classroom.udacity.com/courses/ud730

Gradient-Based Learning Applied to Document Recognition:

http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf

卷积可视化在线演示（blue）：

https://graphics.stanford.edu/courses/cs178/applets/convolution.html

GradientBased Learning Applied to Document Recognition(blue):

http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf

ImageNet Classification with Deep Convolutional Neural Networks

https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

Very Deep Convolutional Networks for Large-Scale Visual Recognition

http://www.robots.ox.ac.uk/~vgg/research/very_deep/

Network In Network：https://arxiv.org/abs/1312.4400

Going Deeper with Convolutions：https://arxiv.org/abs/1409.4842

Deep Residual Learning for Image Recognition

https://arxiv.org/abs/1512.03385

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://arxiv.org/abs/1506.01497

Supervised Sequence Labelling with Recurrent Neural Networks

http://www.cs.toronto.edu/~graves/preprint.pdf

Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs(Blog系列)

http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

https://arxiv.org/abs/1412.3555

A Clockwork RNN：https://arxiv.org/pdf/1402.3511.pdf

Caffe to TensorFlow（模型转换_工具）

https://github.com/ethereon/caffe-tensorflow

Deep Forest: Towards An Alternative to Deep Neural Networks

https://arxiv.org/abs/1702.08835

An implementation of neural style in TensorFlow

https://github.com/anishathalye/neural-style

Magenta is a project from the Google Brain team that asks: use machine learning to create compelling art and music.

https://github.com/tensorflow/magenta

17 Category Flower Dataset(数据集)：http://www.robots.ox.ac.uk/~vgg/data/flowers/17/

Keras: The Python Deep Learning library (Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, orTheano.)

https://keras.io/

Keras: Deep Learning for Python

https://github.com/fchollet/keras

THE MNIST DATABASE of handwritten digits(数据集)

http://yann.lecun.com/exdb/mnist/

Deep Learning Face Representation from Predicting 10,000 Classes（blue）

http://mmlab.ie.cuhk.%20edu.hk/pdf/YiSun_CVPR14.pdf

Face Recognition using Tensorflow (This is a TensorFlow implementation of the face recognizer described in the paper "FaceNet: A Unified Embedding for Face Recognition and Clustering". The project also uses ideas from the paper "A Discriminative Feature Learning Approach for Deep Face Recognition" as well as the paper "Deep Face Recognition" from the Visual Geometry Group at Oxford.)

https://github.com/davidsandberg/facenet

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://arxiv.org/abs/1503.03832

Labeled Faces in the Wild Home (a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13,000 images of faces collected from the web)

http://vis-www.cs.umass.edu/lfw/

Rude Carnie: Age and Gender Deep Learning with TensorFlow

https://github.com/dpressel/rude-carnie

Unfiltered faces for gender and age classification(The OUI-Adience Face Image Project)

http://www.openu.ac.il/home/hassner/Adience/data.html#agegender

Flickr: Find your inspiration(blue)

https://www.flickr.com/

Age and Gender Classification using Convolutional Neural Networks

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.9654&rep=rep1&type=pdf

How-Old.net(识别性别和年龄的网站)

https://how-old.net/#

The Unreasonable Effectiveness of Recurrent Neural Networks