tensorflow neural network

Here is the [1] derivation of the BP algorithm (backpropagation) steps to tidy up, memo Use. [1] the direct use of the matrix differential notation is deduced, the whole process is very concise. And there is a very big advantage of this matrix form is that it is very convenient to implement the programming Control.But its practical scalar calculation deduction also has certain advantages, for example, can clearly know that a weight is affected by who.Marking Conventions:$L $: The number of layer

I've been focusing on CNN implementations for a while, looking at Caffe's code and Convnet2 's code. At present, the content of the single-machine multi-card is more interested, so pay special attention to Convnet2 about MULTI-GPU support.where Cuda-convnet2 's project address is published in: Google Code:cuda-convnet2A more important paper on MULTI-GPU is: one weird trick for parallelizing convolutional neural NetworksThis article will also give an a

1 Introduction An XOR operation is a commonly used calculation in a computer: 0 XOR 0 = 0 0 XOR 1 = 1 1 XOR 0 = 1 1 XOR 1 = 0 We can use the code in the first article to calculate this result Http://files.cnblogs.com/gpcuster/ANN1.rar (need to modify the training set), we can find that the results of learning does not satisfy us, because the single layer of neural network learning ability is limited ,

End-to-end neural network MT (end-to-end Neural machine translation) is a new method of machine translation emerging in recent years. In this paper, we will briefly introduce the traditional method of statistical machine translation and the application of neural network in m

After training a good network, it is important to retrain and predict later. So this article is mainly about if the storage of good parameters and the use of well-trained parameters.The main APIs usedHttps://www.tensorflow.org/api_docs/python/tf/train/Saver The following example illustrates that the network is to construct a convolutional neural

1. Some basic symbols2.COST function================backpropagation algorithm=============1. To calculate something 2. Forward vector graph, but in order to calculate the bias, it is necessary to use the backward transfer algorithm 3. Backward transfer Algorithm 4. Small topic ======== ======backpropagation intuition==============1. Forward calculation is similar to backward calculation 2. Consider only one example, cost function simplification 3. Theta =======implementation Note:unrolling param

In the deep network, the learning speed of different layers varies greatly. For example: In the back layer of the network learning situation is very good, the front layer often in the training of the stagnation, basically do not study. In the opposite case, the front layer learns well and the back layer stops learning.This is because the gradient descent-based learning algorithm inherently has inherent inst

In the previous article "Artificial Neural Network (Artificial neural netwroks) Notes-Eliminate the sample order of the BP algorithm" to modify the weight of the method is called the "steepest descent method." Every time the weight of the changes are determined, the weight will be modified. Even to the simplest single layer perceptron. But we have a question, wh

UFLDL Learning notes and programming Jobs: multi-layer neural Network (Multilayer neural networks + recognition handwriting programming)UFLDL out a new tutorial, feel better than before, from the basics, the system is clear, but also programming practice.In deep learning high-quality group inside listen to some predecessors said, do not delve into other machine l

Http://blog.sina.com.cn/s/blog_98238f850102w7ik.htmlAll the current Ann neural network algorithm Daquan(2016-01-20 10:34:17)reproduced Tags: it Overview1 BP Neural network1.1 Main functions1.2 Advantages and Limitations2 RBF (radial basis function) neural network2.1 Main functions2.2

All the current Ann neural network algorithm DaquanOverview1 BP Neural network1.1 Main functions1.2 Advantages and Limitations2 RBF (radial basis function) neural network2.1 Main functions2.2 Advantages and Limitations3 Sensor Neural Network3.1 Main functions3.2 Advantages a

Now we all know that Geoffrey Hinton's capsule Network (capsule network) shook the entire AI field, pushing the limits of convolution neural networks (CNN) to a new level. There are already a lot of posts, articles and research papers on the web that discuss the theory of capsule networks and how it does better than the traditional CNN. So I'm not going to introd

Artificial neural Network (Artificial neural netwroks) Notes--2.1.3 steps in the discrete multi-output perceptron training algorithm are multiple judgments, so we say it's a discrete multiple output perceptron. Now take the formula Wij=wij+α (YJ-OJ) Xi instead of that step The effect of the difference between Yj and Oj on Wij is manifested by alpha (YJ-OJ) XI

This is an extension of the discrete single output perceptron algorithm Related symbolic definitions refer to the artificial neural network (Artificial neural netwroks) Note-discrete single output perceptron algorithm Ok,start our Game 1. Initialization weight matrix W; 2. Repeat the following process until the training is complete: 2.1 For each sample (X,y)

Recently in the study of Artificial neural network (Artificial neural netwroks), make notes, organize ideas Discrete single output perceptron algorithm, the legendary MP Two-valued Network: The value of the independent variable and its function, the value of the vector component only takes 0 and 1 functions, vectors

+ b.tC. C = a.t + bD. C = a.t + b.t9. Please consider the following code: C results? (If you are unsure, run this lookup in Python at any time). AA = Np.random.randn (3, 3= NP.RANDOM.RANDN (3, 1= a*bA. This will trigger the broadcast mechanism, so B is copied three times, becomes (3,3), * represents the matrix corresponding element multiplied, so the size of C will be (3, 3)B. This will trigger the broadcast mechanism, so B is duplicated three times, becomes (3, 3), * represents matrix multipli

1 Introduction Remember when I first contacted RoboCup 2 years ago, I heard from my seniors that Ann (artificial neural network), this thing can be magical, he can learn to do some problems well enough to deal with. Just like us, we can learn new knowledge by studying. But for 2 years, I've always wanted to learn about Ann, but I haven't been successful. The main reason for this is that the introduction o

0-Background This paper introduces the deep convolution neural network based on residual network, residual Networks (resnets).Theoretically, the more neural network layers, the more complex model functions can be represented. CNN can extract the features of low/mid/high-lev

Welcome reprint, Reprint Please specify: This article from Bin column Blog.csdn.net/xbinworld.Technical Exchange QQ Group: 433250724, Welcome to the algorithm, technology interested students to join.Recently, the next few posts will go back to the discussion of neural network structure, before I in "deep learning Method (V): convolutional Neural

Transfer from http://www.cnblogs.com/heaad/archive/2011/03/07/1976443.htmlThe main contents of this paper include: (1) Introduce the basic principle of neural network, (2) Aforge.net the method of realizing Feedforward neural Network, (3) Matlab to realize the method of Feedforward