Understanding Convolutional Neural Networks in One Article Batch Normalization Dropping Methods
This article describes in detail Batch Normalization and Dropout in Convolutional Neural Networks, including their computational processes, roles, advantages, and how to apply these techniques in the Flying Paddle framework to improve the stability and generalization of the model, as well as providing examples of network structure definition and parameter computation.
Higress Updates: AI Capabilities Open-Sourced and Cloud-Native Capabilities Upgraded
The latest version 1.4 of Higress open-sources a large number of AI-native gateway capabilities based on the accumulated experience of providing AI gateways for Tongyi Qianqian and a number of cloud-based AGI vendors. The latest version 1.4 is based on the accumulated experience of providing AI gateways for Tongyi Qianqian and several cloud AGI vendors, and open-sources a large number of AI-native gateway capabilities.
LMDeploy's Approach to Deploying VLMs and Discussions
LMDeploy Approach and Exploration of Deploying VLMs LMDeploy is an efficient and user-friendly deployment toolkit for Large Language Models (LLMs) and Visual-Language Models (VLMs), developed by the Model Compression and Deployment Team of Shanghai Artificial Intelligence Laboratory (SI-Lab), which covers the functions of model quantization, offline inference, and online services.
Practicing Deep Learning from the Ground Up 2.4 Network Architecture for Handwritten Digit Recognition
This article describes the optimization of network structure design for a handwritten digit recognition task, compares two model structures, a multilayer fully connected neural network and a convolutional neural network, and demonstrates the use of the PaddlePaddle framework to implement these network structures, train them, and observe their performance on the MNIST dataset.
