tensorflow neural network

convolutional Neural Network Primer (1) Original address : http://blog.csdn.net/hjimce/article/details/47323463 Author : HJIMCE convolutional Neural Network algorithm is an n-year-old algorithm, only in recent years because of deep learning related algorithms for the training of multi-layered networks to provide a new

Summary: WithThe artificial neural network has been developed with the development of computational intelligence. The industry now considers that the classification of Neural Networks (NN) in artificial intelligence (AI) may not be appropriate, and that the classification of computational Intelligence (CI) is more descriptive of the problem. Some topics in evolut

The article was transferred from the deep learning public numberDeep learning is a new field in machine learning that is motivated by the establishment and simulation of a neural network for analytical learning of the human brain, which mimics the mechanisms of the human brain to interpret data, examples, sounds and texts. Deep learning is a kind of unsupervised learning.The concept of deep learning derives

1. Recurrent neural Network (RNN) Although the expansion from the multilayer perceptron (MLP) to the cyclic Neural network (RNN) seems trivial, it has far-reaching implications for sequence learning. The use of cyclic neural networks (RNN) is used to process sequence data.

Open source Artificial Neural Network Computing Library FANN Learning Note 1These days machine learning is very fire, neural network is the machine learning algorithm is a more important one. This time I also took some effort, learned a little fur, by the way to do some study notes.There are many textbooks about the ba

In deep learning, regardless of the learning framework, we encounter an important problem, that is, after training, how to store the depth of the network parameters. How these network parameters are invoked at the time of the test. In response to these two questions, this blog post explores how TensorFlow solves them. This blog is divided into three parts, the fi

The introduction of convolution neural network Original address : http://blog.csdn.net/hjimce/article/details/47323463 Author : HJIMCE Convolution neural network algorithm is the algorithm of n years ago, in recent years, because the depth learning correlation algorithm for multi-layer

Objective From the understanding of convolution nerves to the realization of it, before and after spent one months, and now there are still some places do not understand thoroughly, CNN still has a certain difficulty, not to see which blog and one or two papers on the understanding, mainly by themselves to study, read the recommended list at the end of the reference. The current implementation of the CNN in the Minit data set effect is good, but there are some bugs, because the recent busy, the

NIPS 2016 article: Intel China Research Institute on Neural Network compression algorithm of the latest achievementsHttp://www.leiphone.com/news/201609/OzDFhW8CX4YWt369.htmlIntel China Research Institute's latest achievement in the field of deep learning--"dynamic surgery" algorithm 2016-09-05 11:33 reproduced pink Bear 0 reviewsLei Feng Net press: This article is the latest research results of Intel China

An example of TensorFlow convolution neural network Convolution-pool-convolution-pool-Full connection-softmaxforward conduction C1 layer: The input of the convolution neural network is the 28x28 matrix A A, after F1 f_1 5x5 (k1i,..., i=1,2) F1 (k_i^1,..., i=1,2), the convo

this:According to our experience, if the alphabet can be moved to the center of the field of view, the difficulty of recognition will be reduced a lot, in favor of improving the recognition rate.In this case, if we can change the image to the standard size, we can increase the corresponding recognition rate.For objects of real knowledge, from different angles, there will be different manifestations, even for the letter recognition, the letter can appear rotating:If the image can be rotated, the

4 activation function One of the things to be concerned about when building a neural network is what kind of activation function should be used in each separate layer. In logistic regression, the sigmoid function is always used as the activation function, and there are some better choices. The expression for the tanh function (hyperbolic Tangent function, hyperbolic tangent) is: The function image is: Th

Introduction to neural network programming (2): What are we writing during socket writing? Http://www.52im.net/thread-1732-1-1.html 1. IntroductionThis article is followed by the first article titled Neural Network Programming (I): Follow the animation to learn TCP three-way handshakes and four waves, and cont

regression model), the final result is reflected in the data is a straight line or a super plane, But if the data is not linear, the performance of these models will become worse. In view of this problem, there are many algorithms for classifying non-linear data, and neural network is one of the earliest. for a logistic regression model, it can be represented as shown:Where Xi is the individual component o

BP algorithm:　　1. is a supervised learning algorithm, often used to train multilayer perceptron.2. The excitation function required for each artificial neuron (i.e. node) must be micro-(Excitation function: the function relationship between the input and output of a single neuron is called the excitation function.) ）(If the excitation function is not used, each layer in the neural network is simply a linear

non-XOR (the same as 1, the difference is 0), all the output of our training model will be wrong, the model is not linear!2. Neural Network Introduction:We can construct the following models:(where a represents logic with, B is logical or inverse, C is logical OR)The above model is a simple neural network, we have con

Pointer-network is a branch of the recent seq2seq comparison fire, which is widely used in the system of reading comprehension based on deep learning. interested can read the original paper recommended readinghttps://medium.com/@devnag/pointer-networks-in-tensorflow-with-sample-code-14645063f264??This idea is also relatively simple is that the decoded predictions are confined to the input position . it work

LSTM (long-short term Memory, LSTM) is a time recurrent neural network that was first published in 1997. Due to its unique design structure, LSTM is suitable for handling and predicting important events with very long intervals and delays in time series. Based on the introduction of deep learning three Daniel, Lstm network has been proved to be more effective tha

How CNN applies to NLP What is convolution and what is convolution neural network is not spoken, Google. Starting with the application of natural language processing (so, how does any of this apply to NLP?).Unlike image pixels, a matrix is used in natural language processing to represent a sentence or a passage as input, and each row of the matrix represents a token, either a word or a character. So each ro

Although the research and application of neural network has been very successful, but in the development and design of the network, there is still no perfect theory to guide the application of the main design method is to fully understand the problem to be solved on the basis of a combination of experience and temptation, through a number of improved test, finall