simple neural network example

Transfer from http://blog.csdn.net/zouxy09/article/details/8781543CNNs is the first learning algorithm to truly successfully train a multi-layered network structure. It uses spatial relationships to reduce the number of parameters that need to be learned to improve the training performance of the general Feedforward BP algorithm. In CNN, a small part of the image (local sensing area) as the lowest layer of the input of the hierarchy, the information i

is used as the activation function. It performs well in a small number of samples. /* Deep Learning Neural Network V1.0made by xyt2015/7/23 language: This program is used to construct a multi-layer matrix neural network multi-input single output learning strategy: random gradient descent activation function: before u

implementation of the C language of the BP neural network is complete. Finally, we can test the operation of the BP neural network. I am here to give the data, two inputs A, B (10 within the number), an output c,c=a+b. In other words, the BP neural

paid attention to by people. Based on neural network, this paper mainly summarizes some relevant basic knowledge, and then leads to the concept of deep learning, such as where there is improper writing, and please comment on them.1. Neuron modelNeuron is the most basic structure of neural network, it can be said that

convolutional neural Network (CNN) is the foundation of deep learning. The traditional fully-connected neural network (fully connected networks) takes numerical values as input.If you want to work with image-related information, you should also extract the features from the image and sample them. CNN combines features,

should correspond to 0 or 1, so you need to map to 0/1. And if the number of categories increases, then the scope should be expanded accordingly. So, for example, learn: Input as x, such as a picture, Pixel is 34*34pixels, then according to the RGB color principle, a total of 34*34*3 data, so the X is mapped to a (34*34*3, 1) matrix (generally using the conversion matrix of W, that is wt), according to R (34*34), G (34* ), B (34*34) is arranged. The

100, that is, 10^8 parameters. The number of weight connections is reduced by four orders of magnitude compared to the original value.We can easily calculate the output of a network node according to the forward transfer process of BP network signal. For example, for a net input that is labeled as a red node, it is equal to the sum of the product of the weight o

then add a new layer of hidden layer, the original RBM of the hidden layer into its input layer, so that a new RBM, and then the same method to learn its weight value. In addition, multiple RBM can be added to form a deep network (1). The weights learned by the RBM are used as the initial weights of this deep network, and then the BP algorithm is used to study them. This is the learning method of deep beli

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

most advanced results, but see Goodman (2001) combined with many techniques to produce substantial improvements. Obviously, there are more information predictions before words in a sequence, not just the identities of the first few words. At least two feature requirements have been improved in this approach, which we will focus on in this article. First, it does not take into account the context of more than 1 or 2 words, and secondly does not take into account the similarity between words. For

neural network, it is necessary to control the number of trainable parameters of two networks, otherwise there is no comparability. In his machine learning video, Professor Li Hongyi, for example, deep performance is better with the same number of parameters, which means that deep parameters will be less if the same effect is achieved. It is not denied that theo

3. Spark MLlib Deep Learning convolution neural network (depth learning-convolutional neural network) 3.3Http://blog.csdn.net/sunbow0Chapter III Convolution neural Network (convolutional neura