neural lace

Deep Learning Notes (i): Logistic classificationDeep learning Notes (ii): Simple neural network, back propagation algorithm and implementationDeep Learning Notes (iii): activating functions and loss functionsDeep Learning Notes: A Summary of optimization methods (Bgd,sgd,momentum,adagrad,rmsprop,adam)Deep Learning Notes (iv): The concept, structure and code annotation of cyclic neural networksDeep Learning

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 instability, which causes the learning of the front or back layer to stop.Vanishing gradient p

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 learning algorithms, you can directly to learn

"This paper presents a comprehensive overview of the depth of neural network compression methods, mainly divided into parameter pruning and sharing, low rank decomposition, migration/compression convolution filter and knowledge refining, this paper on the performance of each type of methods, related applications, advantages and shortcomings of the original analysis. ” Large-scale neural networks have a la

Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow MNIST convolutional neural network. Https://github.com/nlintz/TensorFlow-Tutorials/blob/master/05_convolutional_net.py.TensorFlow builds a CNN model to train the MNIST dataset. Build a model. Define input data and pre-process data

1. OverviewWe have already introduced the earliest neural network: Perceptron. A very deadly disadvantage of the perceptron is that its linear structure, which can only make linear predictions (even if it does not solve the regression problem), is a point that was widely criticized at the time.Although the perceptual machine can not solve the nonlinear problem, it provides a way to solve the nonlinear problem. The limitation of the perceptron comes fr

Recently, the Google deep Mind team put forward a machine learning model, and a particularly tall on the name: Neural network Turing machine, I translated this article for everyone, translation is not particularly good, some sentences did not read clearly, welcome everyone to criticize Original paper Source: Http://arxiv.org/pdf/1410.5401v1.pdf.All rights reserved, prohibited reprint. Neural netw

Reprint please indicate the Source: Bin column, Http://blog.csdn.net/xbinworldThis is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic reading notes (sentence translation + their o

This is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic reading notes (sentence translation + their own understanding), the contents of the book to comb over, and why the purpose,

Http://www.cnblogs.com/python27/p/MachineLearningWeek05.html This chapter may be the most unclear chapter of Andrew Ng, why do you say so? This chapter focuses on the back propagation (backpropagration, BP) algorithm, Ng spent half time talking about how to calculate the error item δ, how to calculate the δ matrix, and how to use MATLAB to achieve the post transmission, but the most critical question-why so calculate. The previous calculation of these amounts represents what, Ng basically did n

Code (with detailed comments for source code) and dataset can be downloaded in github: Https://github.com/crazyyanchao/TensorFlow-HelloWorld #-*-Coding:utf-8-*-' convolution neural network test mnist data ' ######## #导入MNIST数据 ######## from Tensorflow.examples.tutorials.mnist Import input_data import TensorFlow as tf mnist = input_data.read_data_sets (' mnist_data/', one_hot=true) # Create default Interactiv Esession sess = tf. InteractiveSession (

I've been watching "neural network Design_hagan" Then you want to implement an XOR network yourself. Because the single layer neural network can not divide the different or the judgment to two kinds. According to a^b= (a~b) | (~AB) And I tried it. Or and with both can be solved with sensory neurons, that is, one. Then with and or by the implementation: Hardlim (n) =a, n>=0 when a=1;n Obviousl

First conclusion: When using sigmoid as activating function, cross entropy has the characteristics of fast convergence and global optimization compared to quadratic cost function. Using Softmax as the activation function, Log-likelihood as a loss function, there is no drawback of slow convergence.For the convergence of the loss function, we expect that when the error is greater, the speed of convergence (learning) should be faster. First, quadratic + sigmoid (i), definition Definitions of squar

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), repeat the following procedure: 2.1.1 Inpu

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, whether every time the weight modification is g

If you use 100k batch in this model, and combine the decay of learning rate (that is, the rate of learning is reduced by a ratio every once in a while), the correct rate can be as high as 86%. There are about 1 million parameters to be trained in the model, and the total amount of arithmetic to be estimated is about 20 million times. So this convolution neural network model, using some techniques.(1) Regularization of the L2 of the weight.(2) The imag

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 The advantage of this is that it not only mak

This article is from here, the content of this blog is Java Open source, distributed deep Learning Project deeplearning4j The introduction of learning documents. Introduction:in general, neural networks are often used for unsupervised learning, classification, and regression. That is, neural networks can help group unlabeled data, classify data, or output successive values after supervised training. Th

Tips: This article is a reference to the mechanical industry press "neural network Design" (Dai Qu, etc.) a book compiled by the relevant procedures, for beginners or want to learn more about the neural network kernel enthusiasts, this is the most reading value of the textbook. Perceptual machines and linear neural networks are the simplest and most basic types

3. Spark MLlib Deep Learning convolution neural network (depth learning-convolutional neural network) 3.3Http://blog.csdn.net/sunbow0Chapter III Convolution neural Network (convolutional neural Networks)3 Example3.1 test DataFollow the above example data, or create a new image recognition data.3.2 CNN Example??? //2 te