Artificial neural Network (ANN) is a mathematical model for information processing, which is similar to the structure of synaptic connection in the brain, in which a large number of nodes (or neurons) are connected to form a network, that is, "neural network", in order to achieve the purpose of processing messages, neural networks usually need to be trained, Trai
Original: https://medium.com/learning-new-stuff/how-to-learn-neural-networks-758b78f2736e#.ly5wpz44dThe second post in a series of me trying to learn something new over a short period of time. The first time consisted of learning how to does machine learning in a week.This time I ' ve tried to learn neural networks. While I didn ' t manage to does it within a week, due to various reasons, I did get a basic
This blog will introduce a neural network algorithm package in R: Neuralnet, which simulates a set of data, shows how it is used in R, and how it is trained and predicted. Before introducing Neuranet, let's briefly introduce the neural network algorithm .Artificial neural Network (ANN), or neural network, is a mathemat
Convolution Neural network
Convnets is used to process data with multiple array formats, such as a color image consisting of three two-dimensional arrays, which contains pixel intensities on three color channels. Many data forms are in the form of multiple arrays: one-dimensional signals and sequences, including languages; Two-dimensional image or audio spectrum, three-dimensional video or stereo image. Convnets has four key ideas that take advantage
Recurrent neural NetworksIn traditional neural networks, the model does not focus on the processing of the last moment, what information can be used for the next moment, and each time will only focus on the current moment of processing. For example, we want to classify the events that occur at every moment in a movie, and if we know the event information in front of the movie, then it is very easy to classi
Sample program Download: Http://files.cnblogs.com/gpcuster/ANN3.rarIf you have questions, please refer to the FAQIf you do not find a satisfactory answer, you can leave a message below:)0 CatalogueIntroduction to Artificial neural network (1)--application of single-layer artificial neural networkIntroduction to Artificial neural network (2)--the basic concept of
. The artificial intelligence technology in game programming (serial one)
Introducing neural networks in normal language(Neural Networks in Plain 中文版)
Because we do not have a good understanding of the brain, we often try to use the latest technology as a model to explain it. When I was a child, we all believed that the brain was a telephone switch. (Otherwise, what else could it be?) I also saw
Currently, Java is used to develop the largest number of ape programs, but most of them are limited to years of development. In fact, Java can do more and more powerful!
I used Java to build a [self-built neural network] instead of laboratory work, it is a real, direct application that makes our programs smarter, let our program have the perception or cognitive function! Do not use the same number as the neural
Abstract: With the development of computational intelligence, artificial neural network has been developed. The industry now considers that it may not be appropriate to classify neural networks (NN) in artificial intelligence (AI), and that the classification of computational Intelligence (CI) can explain the nature of the problem. Some topics in evolutionary computing, artificial life and fuzzy logic syste
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 from the research of artificial
In the first two sections, the logistic regression and classification algorithms were introduced, and the linear and nonlinear data sets were classified experimentally. Logistic uses a method of summation of vector weights to map, so it is only good for linear classification problem (experiment can be seen), its model is as follows (the detailed introduction can be viewed two times blog:
linear and nonlinear experiments on logistic classification of machine learning (continued)):
That being 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:
The Tanh function is actually a shifted version
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. In the traditional neural network model, the
This paper summarizes the notes based on the series of machine learning techniques in Taiwan.The main content is as follows:Firstly, the structure of hypothesis and network of radial basis function network is introduced, then the RBF Neural Network learning algorithm is introduced, and the learning by using K-means is studied, and finally the understanding and understanding of this neural network is deepene
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AI technology in game programming
. (Serialization)
Introduce Neural Networks in common languages(Neural Networks in plain English)
Because we don't have a good understanding of the brain, we often try to use the latest technology as a model to explain it. In my childhood, we all believed that the brain was a telephone switch. (What else can it be ?) At that time, I also saw Xie Linton, a famou
reversal of the convolutional neural network. For example, enter the word "cat" to train the network by comparing the images generated by the network with the real images of the cat, so that the network can produce images more like the cat. DN can be combined with ffnn like conventional CNN, so you need to give it a new "abbreviation. The term "deep anti-convolutional network" is probably feasible, but you may argue that two different names should be
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 later.Neural networks are widely used in machine learning, such as function approximation, pattern recognition, classi
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 row is a vector, and this vector can be a word
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
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 constructed three perceptron, and the output of two perceptron as another input to perceive i
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