what activation function

RNN, also known as recurrent neural Network, is a nonlinear dynamic system that maps sequences to sequences with five main parameters:[WHV, WHH, Woh, BH, Bo, H0], typical structural diagrams are as follows:Explain: like normal neural

http://m.blog.csdn.net/blog/wu010555688/24487301This article has compiled a number of online Daniel's blog, detailed explanation of CNN's basic structure and core ideas, welcome to exchange.[1] Deep Learning Introduction[2] Deep Learning training

There are many contents about the geometric Classifier in the book, including linear classifier and non-linear classifier. Linear classifiers include sensor algorithms, incremental correction algorithms, lmse classification algorithms, and Fisher

Preface: Keep your style consistent. Before you officially start writing, start with a long talk. There are too many books and articles about neural networks, so I am not allowed to talk about them in a word that is too arrogant. I try to write a

TroubleAs a data analyst, you have been working in this multinational bank for half a year.This morning, the boss called you to the office, dignified complexion.You play the drums in your heart and you think you've stabbed something. Fortunately,

0 Preface Neural network in my impression has been relatively mysterious, just recently learned the neural network, especially the BP neural network has a more in-depth understanding, therefore, summed up the following experience, hoping to help

Network in Network learning notes -lenet and other traditional CNN network of the convolution layer is actually using linear filter to the image of the internal product operation, after each local output followed by a non-linear activation function,

lstm Neural network in simple and lucid Published in 2015-06-05 20:57| 10,188 Times Read |  SOURCE http://blog.terminal.com| 2 Reviews | Author Zachary Chase Lipton lstm Recurrent neural network RNN long-term memory Summary:The LSTM network has

Some time ago read some about the lstm aspect of the paper, has been prepared to record the learning process, because other things, has been dragged to the present, the memory is fast blurred. Now hurry up, the organization of this article is like

Understanding the difficulty of training deep feedforward Neural Understanding the difficulty of training deep feedforward Neural Networks Overview Sigmod experiment cost function influence weights initialization Summary Neural networks are

http://blog.csdn.net/linmingan/article/details/50958304 The inverse propagation algorithm of cyclic neural networks is only a simple variant of the BP algorithm. First we look at the forward propagation algorithm of cyclic neural networks: It

Depth network overview Contents [hide] 1 Overview 2 Depth Network Advantages 3 training Depth Network difficulties 3.1 data acquisition Problem 3.2 Local extremum problem 3.3 Gradient dispersion problem 4 Layer Greedy training method 4.1 data get 4.2

From today onwards, the formal step into the ranks of in-depth study. have been touched before, but not system. This is the beginning of the cs231 course. Why Mini-batch gradient descent can work

Search, Street View, photos, translations, the services Google offers, use Google's TPU (tensor processor) to speed up the neural network calculations behind it. On the PCB board Google's first TPU and the deployment of the TPU data center Last year,

The first part: Caffe introductionCaffe is a Berkeley visual and Learning Center (BVLC) developed. The author is Dr. Berkeley Jia Yangqing.Caffe is a deep learning (learning) framework. It has easy-to-read, fast and modular thinking.Part II: Caffe

Linux from program to processVamei Source: Http://www.cnblogs.com/vamei Welcome reprint, Please also keep this statement. Thank you!How does the computer execute the process? This is the core issue of computer operation. Even if the program has been

Http://www.jianshu.com/p/75f7e60dae95Chen Dihao Source: CSDNHttp://dataunion.org/26447.htmlIntroduction to cross-entropyCrossover Entropy (cross Entropy) is a kind of loss function (also called loss function or cost function), which is used to

A summary of the classic network of CNN convolutional Neural NetworkThe following image refers to the blog: http://blog.csdn.net/cyh_24/article/details/51440344Second, LeNet-5 network Input Size: 32*32 Convolution layer: 2 Reduced

Linear regressionGiven a data point set X and the corresponding target value Y, the goal of the linear model is to find a use vector W and displacement BThe lines described, to be as close as possible to each sample x[i] and Y[i].The mathematical

CNN began in the 90 's lenet, the early 21st century silent 10 years, until 12 Alexnet began again the second spring, from the ZF net to Vgg,googlenet to ResNet and the recent densenet, the network is more and more deep, architecture more and more