Disclaimer: This article is for learning and communication purposes only, and should not be used as a reference for auction bidding! Neural networks are used to find the weight relationship between inputs and outputs based on existing data (approximately), and then utilize this weight relationship for simulation. The output value is set as the minimum … Read more
Author: Wu Xiaoyi Column Author of Python Enthusiasts Community Personal Blog: Wu Xiaoyi Zhihu Profile: Wu Xiaoyi丶 WeChat Official Account: Data Road (shuju_lu) Code and Data Acquisition Method: Follow the WeChat Official Account “Python Enthusiasts Community” and reply:Practical Mining This article is the practical part of the book “Python Data Analysis and Mining Practical”,整理分析后的复现. This … Read more
The BP neural network (Back Propagation) is a multi-layer network that propagates signals forward and errors backward. This network has very strong computational capabilities and can solve many nonlinear problems by analyzing the nonlinear mapping relationship between input and output variables through training samples. 1. Structure of BP Neural Network The structure of the BP … Read more
(Click the official account above, you can quickly follow) Source: taogang Link: http://my.oschina.net/taogang/blog/686568?fromerr=DzNrW1c1 Introduction to Neural Networks A neural network attempts to simulate the relationships between neurons in the brain to process information. Its computational model typically requires a large number of interconnected nodes. Each neuron processes weighted input values from other neighboring neurons through … Read more
This article will coverthe essence of Attention,the principle of Attention and its applications to help you understand the Attention mechanism. 1.The Essence of Attention The core logic: from focusing on everything to focusing on key points The Attention mechanism can grasp the key points when processing long texts, without losing important information. The Attention mechanism … Read more
With the rapid popularity of the Transformer model, Self-Attention and Multi-Head Attention have become core components in the field of Natural Language Processing (NLP).This article will analyze these two attention mechanisms from three aspects: brief introduction, workflow, and comparison. 1. Brief Introduction Self-Attention: Allows each element in the input sequence to focus on and weight … Read more
200,000, this is the total number of users New Intelligence has reached today. On the journey to an intelligent universe, we thank every friend who travels with New Intelligence. Your attention and support is the inexhaustible fuel for the “New Intelligence” starship. 200,000, every passenger is invaluable to us. We hope to deepen our understanding … Read more
This article will coverthe essence of Gradient Descent, the principles of Gradient Descentand the algorithms of Gradient Descent in three aspects, helping you understand Gradient Descent Gradient Descent | GD. Gradient Descent 1.Essence of Gradient Descent Machine Learning’s “Three Essentials”: Select a model family, define a loss function to quantify prediction errors, and find the … Read more
This article will cover the essence of ViT and the principles of ViT, as well as the applications of ViT to help you understand Vision Transformer |ViT. Vision Transformer (ViT) 1. ViTessence Definition of ViT:ViT brings the Transformer architecture from the natural language processing domain into computer vision for processing image data. In the field … Read more
This article will explain the essence, principle, and applications of diffusion models, helping you to understand diffusion models in one go. Diffusion Models The essence of diffusion models Definition of diffusion models:Diffusion Models are a new, advanced generative model used to generate data similar to the training data, capable of generating various high-resolution images. The … Read more