1 Introduction to algorithm
1.1 principle of BP neural network algorithm affected by relevant indexes
As shown in Figure 1, when training BP using MATLAB's newff function, it can be seen that most cases are three-layer neural networks (i.e. input layer, hidden layer and output layer). Here to help understand the principle of neural network: 1 ...
Posted by tzzoug on Sat, 18 Sep 2021 19:52:24 -0700
Impulse neural network is called the third generation neural network, which has higher biological reliability. SNN has always occupied the core position in the research of brain like science in recent years. When the performance is similar, the chip based on pulse neural network has lower power consumption, better stability and robustness than ...
Posted by coverman on Thu, 16 Sep 2021 11:38:05 -0700
Introduction of BP network prediction algorithm
1. Introduction to Artificial Neural Network
Artificial Neural Network (ANN) is a computer system formed by several very simple processing units connected to each other in some way to mimic the structure and function of the human brain. It processes information by the dynamic response of its sta ...
Posted by Jim_Bo on Tue, 14 Sep 2021 09:13:02 -0700
1.1 case introduction
In this case, pytoch is used to build a DenseNet network structure for image classification of fashion MNIST dataset. The analysis of this problem can be divided into data preparation, model establishment, training with training set and testing the effect of model with test set.
1.2 environment configurat ...
Posted by OhLordy on Fri, 10 Sep 2021 01:45:07 -0700
Fundamentals of machine learning
The essence of machine learning: using data to solve problems
Data preprocessing (important in deep learning) - > training phase - > model generation - > prediction phase
We usually choose some data as the test set, such as about 20%.
Sometimes there is an additional verification set of about 20 ...
Posted by hyngvesson on Wed, 08 Sep 2021 00:38:35 -0700