lstm neural network

first, the Origin Originally wanted to follow the traditional recursive algorithm to achieve maze game--> genetic algorithm to achieve maze game--> neural network maze game ideas, in this article also write how to use the neural network to achieve the maze, but the study, feel some trouble is not very good, so I chose

Article reproduced from: http://www.52analysis.com/R/1627.html Neural Network (optimization algorithm) Artificial neural Network (ANN), referred to as neural network, is a mathematical model or computational model that mimics th

Multi-Task confrontation learning [1] In order to gain robustness against noise, multi-task learning is introduced into three networks:-Input Network (green), used as feature extractor-Senone output Network (red), used as Senone classification-Domain output Network (blue), domain here refers to the type of noise, a total of 17 kinds of noise In order to increase

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 feedfor

Introduction to neural network programming (2): What are we writing during socket writing? Http://www.52im.net/thread-1732-1-1.html 1. IntroductionThis article is followed by the first article titled Neural Network Programming (I): Follow the animation to learn TCP three-way handshakes and four waves, and cont

regression model), the final result is reflected in the data is a straight line or a super plane, But if the data is not linear, the performance of these models will become worse. In view of this problem, there are many algorithms for classifying non-linear data, and neural network is one of the earliest. for a logistic regression model, it can be represented as shown:Where Xi is the individual component o

Summary:On March 13, 2018, the Shen Junan community, from Harbin Institute of Technology, shared a typical model-an introduction to deep neural networks. This paper introduces the development course of deep neural network in detail, and introduces the structure and characteristics of each stage model in detail.The Shen Junan of Harbin Institute of Technology shar

BP algorithm:　　1. is a supervised learning algorithm, often used to train multilayer perceptron.2. The excitation function required for each artificial neuron (i.e. node) must be micro-(Excitation function: the function relationship between the input and output of a single neuron is called the excitation function.) ）(If the excitation function is not used, each layer in the neural network is simply a linear

non-XOR (the same as 1, the difference is 0), all the output of our training model will be wrong, the model is not linear!2. Neural Network Introduction:We can construct the following models:(where a represents logic with, B is logical or inverse, C is logical OR)The above model is a simple neural network, we have con

Source: Michael Nielsen's "Neural Network and Deep leraning", click the end of "read the original" To view the original English.This section translator: Hit Scir master Xu Wei (https://github.com/memeda)Statement: We will be in every Monday, Thursday, Sunday regularly serialized the Chinese translation of the book, if you need to reprint please contact [email protected], without authorization shall not be r

Microsoft Research Asia chief researcher Sun JianHow accurate is the world's best computer vision system? On December 10 9 o'clock in the morning EST, the imagenet Computer Vision Recognition Challenge was announced--Microsoft Research Asia Vichier's researchers, with the latest breakthroughs in deep neural network technology, have won the title of all three major projects with absolute advantage in image c

1. Background:1.1 Inspired by neural networks in the human brain, there have been many different versions in history. 1.2 The most famous algorithms are the backpropagation of the 1980.2. Multilayer forward neural networks (multilayer feed-forward neural network)The 2.1 backpropagation is used on a multilayer forward