neural network book

The linear neural network is similar to the perceptron, but the activation function of the linear neural network is linear rather than the hard transfer function, so the output of the linear neural network can be any value, and th

"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

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

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

Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow MNIST convolutional neural network. Https://github.com/nlintz/TensorFlow-Tutorials/blob/master/

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

really simple, very mathematical beauty. Of course, as a popular science books, it will not tell you how harmful this method is.Implementation, you can use the following two algorithms:①KMP: Put $w_{i}$, $W _{i-1}$ two words together, run once the text string.②ac automaton: Same stitching, but pre-spell all the pattern string, input AC automaton, just run once text string.But if you are an ACM player, you should have a deep understanding of the AC automaton, which is simply a memory killer.The

Written in front: Thank you @ challons for the review of this article and put forward valuable comments. Let's talk a little bit about the big hot neural network. In recent years, the depth of learning has developed rapidly, feeling has occupied the entire machine learning "half". The major conferences are also occupied by deep learning, leading a wave of trends. The two hottest classes in depth learning ar

First, the main method of neural network performance tuning the technique of data augmented image preprocessing network initialization training The selection of activation function different regularization methods from the perspective of data integration of multiple depth networks 1. Data augmentation The generalization ability of the model can be improved by inc

A feedforward neural network is a artificial neural network wherein connections the the between does not form a units. As such, it is different from recurrent neural networks.The Feedforward neural

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

Why use sequence models (sequence model)? There are two problems with the standard fully connected neural network (fully connected neural network) processing sequence: 1) The input and output layer lengths of the fully connected neural n