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On explainability of Deep Neural networks«learning F # Functional Data structures and algorithms is out! On explainability of deep neural NetworksDuring a discussion yesterday with software architect Extraordinairedavid Lazarregardinghow Everything old is new again, the topic of deep neural networks and its amazing success were brought up. Unless one isliving und

ObjectiveThis article continues our Microsoft Mining Series algorithm Summary, the previous articles have been related to the main algorithm to do a detailed introduction, I for the convenience of display, specially organized a directory outline: Big Data era: Easy to learn Microsoft Data Mining algorithm summary serial, interested children shoes can be viewed, Before starting the Microsoft Neural Network analysis algorithm, this article first makes a

convolutional Neural Network (convolutional neural networks/cnn/convnets)Convolutional neural networks are very similar to normal neural networks: the neurons that make up them all have learning weights (weights) and biases (biases). Each neuron accepts some input, performs a dot product operation, and may execute a no

First, what is an artificial neural network? Simply put, a single perceptron as a neural network node, and then use such nodes to form a hierarchical network structure, we call this network is the artificial neural network (I own understanding). When the level of the network is greater than or equal to 3 layers (input layer + hidden layer (greater than or equal t

I. CNN's biological principles, applications and advantagesCNN based on the local characteristics of the human eye Vision nerve design, widely used in image image, pattern recognition, machine vision and speech recognition, it on the image translation, scaling, rotation and other deformation has a high degree of invariance. In a word, the core idea of CNN is to combine the three ideas of local sensation field, weighted value sharing, time or space sub-sampling to obtain some degree of translatio

1. Data preprocessingbefore training the neural network, it is necessary to preprocess the data, and an important preprocessing method is normalization processing. The following is a brief introduction to the principle and method of normalization processing. (1) What is normalization?Data normalization is the mapping of data to [0,1] or [ -1,1] intervals or smaller intervals, such as (0.1,0.9).(2) Why should normalization be processed?Input with a lar

convolutional Neural NetworksReprinted from: http://blog.csdn.net/stdcoutzyx/article/details/41596663Since July this year, has been in the laboratory responsible for convolutional neural networks (convolutional neural network,cnn), during the configuration and use of Theano and Cuda-convnet, Cuda-convnet2. In order to enhance the understanding and use of CNN, thi

convolutional Neural NetworksReprint Please specify: http://blog.csdn.net/stdcoutzyx/article/details/41596663Since July this year, has been in the laboratory responsible for convolutional neural networks (convolutional neural network,cnn), during the configuration and use of Theano and Cuda-convnet, Cuda-convnet2. In order to enhance the understanding and use of

AboutNeural networks is one of the most beautiful programming paradigms ever invented. In the conventional approach to programming, we'll tell the computer, "What to do," breaking big problems up into many small, PR Ecisely defined tasks that the computer can easily perform. By contrast, in a neural network we don't tell the computer what the solve our problem. Instead, it learns from observational data, figuring out its own solution to the problem at

Summary of Ann Training algorithm based on traditional neural networkLearning/Training Algorithm classificationThe different types of neural networks correspond to different kinds of training/learning algorithms. Therefore, according to the classification of neural networks, the traditional neural network learning algo

Learning/Training Algorithm classification The different types of neural networks correspond to different kinds of training/learning algorithms. Therefore, according to the classification of neural networks, the traditional neural network learning algorithms can be divided into the following three categories: 1 feedforward Ne