tensorflow neural network

A reference to the artificial neural network should think of three basic knowledge points: One is the neuron model, the other is the neural network structure, and the third is the learning algorithm. There are many kinds of neural networks, but the classification basis canno

, Q2 is closer to P, and its cross-entropy is smaller.In addition, the cross-entropy has another form of expression, or the use of the above hypothetical conditions: The result is: All of the above instructions are for a single sample case, and in the actual use of the training process, the data is often combined into a batch to use, so the output of the neural network used should be a m*n two-dimensional

bp neural network in BP for back propagation shorthand, the earliest it was by Rumelhart, McCelland and other scientists in 1986, Rumelhart and in nature published a very famous article "Learning R Epresentations by back-propagating errors ". With the migration of the Times, the theory of BP neural network has been imp

Deep learning--the artificial neural network and the upsurge of researchHu XiaolinThe artificial neural network originates from the last century 40 's, to today already 70 years old. Like a person's life, has experienced the rise and fall, has had the splendor, has had the dim, has had the noisy, has been deserted. Gen

At present, deep learning (Deepin learning, DL) in the field of algorithm is rounds, now is not only the Internet, artificial intelligence, the life of the major areas can reflect the profound learning led to the great change. To learn deep learning, first familiarize yourself with some basic concepts of neural networks (neural Networks, referred to as NN). Of course, the

Introduction of artificial neural network and single-layer network implementation of and Operation--aforge.net Framework use (v)The previous 4 article is about the fuzzy system, it is different from the traditional value logic, the theoretical basis is fuzzy mathematics, so some friends looking a little confused, if interested in suggesting reference related book

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 Process[3] Deep learning Model: the derivation and implementation of CNN convolution neural network[4] Deep learning Model: the reverse derivation and practice of

First, prefaceAfter a period of accumulation, for the neural network, has basically mastered the Perceptron, BP algorithm and its improvement, Adaline and so on the most simple and basic knowledge of feedforward neural network, the following is based on the feedback neural

the hidden state, and relies on gates to control. Gates ' control basis: The three gates used in the lstm described above are based on wxt+wht−1 W x t + W h t−1, but can be increased by connection to the memory cell or by deleting a gate's wxt W x t or wht−1 W H t−1 to reduce the control basis. For example, remove the ht−1 H t−1 in Zt=sigmoid (wz⋅[ht−1,xt]) Z t = S i g M o i d (W z⋅[h t−1, X T]) in the above image to Zt=sigmoid (wz⋅ht−1) Z t = s i g M o i d (W z⋅h t−1) After the introduction o

Artificial neural Network (ANN), or neural network, is a mathematical model or a computational model for simulating the structure and function of biological neural networks. Neural networks are computed by a large number of artifi

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 proven to be more effective t

Introduction to machine learning--talking about neural network This article transferred from: http://tieba.baidu.com/p/3013551686?pid=49703036815see_lz=1#Personal feel is very full, especially suitable for contact with neural network novice. Start with the question of regression (Regression). I have seen a lot of peopl

absrtact : This paper will analyze the basic principle of deep neural network to recognize graphic images in detail. For convolutional neural Networks, this paper will discuss in detail the principle and function of each layer in the network in the image recognition, such as the convolution layer (convolutional layers)

Recently, the journal Platform Distill published an article by Google researchers, introducing a powerful tool for neural network visualization and style migration: micro-image parameterization. This article describes the tool in several ways. Image Classification Neural network has excellent image generation capa

achieved the ImageNet 6.67% the results.2.2 Inception V2Inception V2 learned that the Vgg used two 3′3 convolution instead of the large convolution of 5′5, and built more nonlinear transformations while reducing the parameters, making CNN more capable of learning features:Two 3′3 convolution layer functions similar to a 5′5 convolution layerIn addition, the famous Batch normalization(hereinafter referred to as BN) method is proposed. BN is a very effective regularization method, which can accel

absrtact : This paper will analyze the basic principle of deep neural network to recognize graphic images in detail. For convolutional neural Networks, this paper will discuss in detail the principle and function of each layer in the network in the image recognition, such as the convolution layer (convolutional layers)

single unit with a complex memory unit .??TensorFlow examples of LSTMHttps://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/tutorials/recurrent/index.mdhttp://colah.github.io/posts/2015-08-Understanding-LSTMs/It is mentioned herethat RNN can learn historical information when the distance is short, but RNN is powerless when the distance is longer . example of a short distance, predicting skylo

Constructing neural network with Keras Keras is one of the most popular depth learning libraries, making great contributions to the commercialization of artificial intelligence. It's very simple to use, allowing you to build a powerful neural network with a few lines of code. In this article, you will learn how to bui