Programmer Group

Cost function: quadratic, cross entropy, log likelihood (course: refining numbers into gold) quadratic cost function C = 1 2 n ...

In this article, we will explore how to build a simple network model with triple loss. It is widely used in face verification, face recognition and signature verification. Before entering the code, let's learn what triple loss is and how to implement it in PyTorch. Triple loss Triple loss function is a widely used loss function at present. It ...

Modern neural network This week, I mainly learned several classic modern neural networks: AlexNet, VGG, NiN, geoglenet and ResNet. These networks can be regarded as a process of continuous refinement in the development of neural networks: Starting from the LeNet network, AlexNet is generated by changing the activation function and using DropOu ...

         After selecting the architecture and setting the super parameters, we enter the training stage. Our goal is to find the parameter value that minimizes the loss function. After training, we need to use these parameters to make future predictions. Sometimes we need to extract parameters to reuse th ...

1 Introduction to algorithm 1.1 introduction to the principle of BP neural network Neural network is the basis of deep learning. It is widely used in machine learning and deep learning, such as function approximation, pattern recognition, classification model, image classification, CTR prediction based on deep learning, data compression, data ...

         TensorRT is designed by Nvidia to accelerate the reasoning based on its own GPU training model. After we train the model, TensorRT can directly conduct one-to-one correspondence on the network layer of the model, so as to accelerate the reasoning deployment of larger models. Recently, TensorRT ha ...

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 ...

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 ...

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 ...

1. Preface 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 ...