perceptron neural network

Deep Learning Neural Network pure C language basic edition, deep Neural Network C Language Today, Deep Learning has become a field of fire, and the performance of Deep Learning Neural Networks (DNN) in the field of computer vision is remarkable. Of course, convolutional

P1038 neural network and p1038 Neural NetworkBackground Artificial Neural Network (Artificial Neural Network) is a new computing system with self-learning ability. It is widely used in

accordance with the needs of the chapters to learn, so always anxious. To the original most important part of the basic is not mastered directly to learn the new network structure and new models, which leads to low learning efficiency, until in the study encountered a bottleneck, just back to look at the Han Liqun Teacher's "Artificial Neural network Tutorial" i

Data classification based on BP Neural network BP (back propagation) network is the 1986 by the Rumelhart and McCelland, led by the team of scientists, is an error inverse propagation algorithm training Multilayer Feedforward Network, is currently the most widely used neural

connection mode of the network, the weight value and the excitation function are different. The network itself is usually the approximation of some kind of algorithm or function in nature, and it may be the expression of a logical strategy.A. PerceptronThe Perceptron is equivalent to a single layer of a neural

"This paper presents a comprehensive overview of the depth of neural network compression methods, mainly divided into parameter pruning and sharing, low rank decomposition, migration/compression convolution filter and knowledge refining, this paper on the performance of each type of methods, related applications, advantages and shortcomings of the original analysis. ” Large-scale

Deep Learning Notes (i): Logistic classificationDeep learning Notes (ii): Simple neural network, back propagation algorithm and implementationDeep Learning Notes (iii): activating functions and loss functionsDeep Learning Notes: A Summary of optimization methods (Bgd,sgd,momentum,adagrad,rmsprop,adam)Deep Learning Notes (iv): The concept, structure and code annotation of cyclic

First, you need to familiarize yourself with how to use pytorch to implement a feed-forward neural network. To facilitate understanding, we only use a feed-forward neural network with only one hidden layer as an example: The source code and comments of a feed-forward neural

Self-organizing neural network, also known as self-organizing competitive neural network, is especially suitable for solving the problem of pattern classification and recognition. The network model belongs to the Feedforward neural