# March, 2017

## 美国斯坦福大学发布2025计划 颠覆全球高等教育

2017-03-30 09:40:39 来源: 网易教育 《斯坦福大学2025计划》在以设计思考理论著称的斯坦福大学设计学院牵头下正式启动，这次教育改革改变了以往自上而下的方式，代之以师生为主导......

## TensorFlow 2. Shadow CNN example for MNIST data

The practice is to understand how Tensorflow applied to shadow NN in MNIST data. The practice is from Big Data University lectures. Reference: Support_Vector_Machines.html  (Coursera Machine Learning Course) Big Data University TensorFlow course Deep Learning Concept  Using multiple process layer with non-linear algorithm to simulate brain ability;  A branch of machine learning. We will focus on shadow NN in this note. Shadow NN MNIST Example: two or three layers only. In the context of supervised learning, digits recognition in our case, the learning consists of a target/feature which is toRead More

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## TensorFlow 101C. Image Texture

This Note is for image texture explanation: Reference  https://courses.cs.washington.edu/courses/cse576/book/ch7.pdf  (computer vision) Why Texture Texture gives us information about the spatial arrangement of the colors or intensities in an image. Why? The answer is the histogram can’t fully represent/classify images. All images below are half white and half black. However, the images are different. How to recognize texture Structural approach: Texture is a set of primitive texels in some regular or repeated relationship. Statistical approach: Texture is a quantitative measure of the arrangement of intensities in a region. Statistical method Co-occurrenceRead More

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## TensorFlow 3. A Small and Interest word2vec NN example to start your adventure

The note intention is to understand the word2vec, and how to build a small NN to start your adventure on Deep Learning. You can see many source codes here to build the NN. But I am not yet built it with TF. Reference: A visual toy NN network for word2vec generation. The whole source code is from https://ronxin.github.io/wevi/.  The source code is written by javascript for NN with html and svg (https://www.tutorialspoint.com/d3js/d3js_introduction_to_svg.htm) for graph visibility.  Try your best to understand all thoroughly (not just well enough). It will help youRead More

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## TensorFlow 101B. CNN Concept

The note is to understand the concept/rise of CNN. Reference: http://colah.github.io/posts/2014-07-Conv-Nets-Modular/ Introduction convolutional neural network Lot’s of same neurons, similar as java function, which can be re-use X is the input layer (you can sense that is see/hear/smell, etc. for example, image, video, audio, document) Next Layer is not always fully connected with previous layer:  one neuron of type A neuron is not fully connected to each X.    B is not fully connected with All A  F is fully connected with all B Why so many same neurons? ThatRead More

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## 比尔·盖茨北大演讲:中国年轻人处在一个绝佳时代

2017-03-24 21:03:43 来源: 南方都市报(深圳) 比尔·盖茨北京大学演讲稿全文（2017年3月24日）） 中国的未来：创新、慈善与全球领导力   很高兴来到北大，特别是在北大即......

## TensorFlow 101A. Manual Convolution Calculation

The note is to describe how to calculate Convolution via manual or TensorFlow command. TensorFlow convolution common commands: y= np.convolve(x,h,”valid”) and y= np.convolve(h,x,”valid”)  are same…also true for “same”,”full” options. from scipy import signal as sg   sg.convolve is using FFT which is faster than np.convolve for big matrix convolution inverse = numpy.linalg.inv(x)  One dimension with zero padding When we apply kernel, we always use kernel’s inverse to multiply and sum. One dimension without zero padding  One dimension filter with multiple dimensions of input x= [[255,   7,  3],    Read More

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