what activation function

Given a training set of M training samples, the gradient descent method is used to train a neural network, and for a single training sample (x,y), the loss function of the sample is defined:So the loss function for the entire training set is defined

Fancy explanations for Autoencoder and vae What is an automatic encoderThe Automatic encoder (Autoencoder) is initially used as a compression method for data, which has the following characteristics:1) high correlation with the data, which means

Based on the traditional polynomial regression, neural network is inspired by the "activation" phenomenon of the biological neural network, and the machine learning model is built up by the activation function.In the field of image processing,

LSTM Hidden Neuron structure: Detailed structure of lstm hidden neurons: Let the program itself learn whether to carry, so learn to add #include "iostream" #include "math.h" #include "stdlib.h" #include "time.h" #include "vector" #inc Lude "Assert.

The foundation of deep learning--the beginning of neural network Original address fundamentals of Deep learning–starting with Artificial neural network preface Deep learning and neural networks are now driving advances in computer science, both of

AttentionRefer to the validation set. Trainset loss can usually be lowered, but validation set loss begins to rise gradually after a period of reduction, when the model begins to fit on the training set. Focusing on Val loss changes, Val acc may

First conclusion: When using sigmoid as activating function, cross entropy has the characteristics of fast convergence and global optimization compared to quadratic cost function. Using Softmax as the activation function, Log-likelihood as a loss

This digest from: "Pattern recognition and intelligent computing--matlab technology implementation of the third edition" and "Matlab Neural network 43 Case Analysis" "Note" The Blue font for your own understanding part The advantages of radial basis

LSTM (long-short term Memory, LSTM) is a time recurrent neural network that was first published in 1997. Due to its unique design structure, LSTM is suitable for handling and predicting important events with very long intervals and delays in time

1.why Look in case study This week we'll talk about some typical CNN models, and by learning these we can deepen our understanding of CNN and possibly apply them in practical applications or get inspiration from them. 2.Classic Networks The LENET-5

Reprint: http://www.cnblogs.com/zhijianliutang/p/4050931.htmlObjectiveThis 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

Database Introduction Development tools Network framework Training results Training Essentials Activation function The role of dropout Training Code "Original" Liu_longpoReprint Please specify the

This series of articles is the study notes of "machine learning", by Prof Andrew Ng, Stanford University. This article is the notes of week 5, neural Networks learning. This article contains some topic on cost Function and backpropagation

The principle of RBF neural networks has been introduced in my blog, "RBF Neural Network for machine learning", which is not repeated here. Today is to introduce the common RBF neural Network learning Algorithm and RBF neural network and multilayer

"Install Anaconda3"Download: https://www.continuum.io/downloads, prompts during installation failed to create Anacoda menue refer to Http://www.cnblogs.com/chuckle/p/7429624.html when the error occurs. "Install TensorFlow"(Requires network link,

Online there are many simple rnn bptt algorithm derivation. Let's arrange it with your own marks.I had a habit of using the subscript to indicate the sample number, which can no longer be represented here, because the subscript needs to be used to

This article is from here, the content of this blog is Java Open source, distributed deep Learning Project deeplearning4j The introduction of learning documents. Introduction:in general, neural networks are often used for unsupervised learning,

Introduction to Anti-NN Concept Introduction The origin of the name and the process of confrontation A model against NN Models and training to combat nn Discriminating the optimal value of network D Gaussian distribution of

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:

The structure of this article: What is a linear unit What's the use? Code implementation 1. What is a linear unitThe difference between a linear element and a perceptron is in the activation function:The f of the