Source:AI 有道 Approximately 3200 words, recommended reading time 5 minutes This article introduces the mathematical principles behind neural networks. Neural networks are a clever arrangement of linear and nonlinear modules. By smartly selecting and connecting these modules, we obtain a powerful tool to approximate any mathematical function, such as a neural network that can classify … Read more
Have you noticed that neural networks are everywhere? They appear in the news, in your phone, and even on your social media. But honestly, most of us don’t know how they work. Those fancy math and strange terms like “backpropagation”? In this article, we explore the Multi-Layer Perceptron (MLP) – the most basic type of … Read more
Author: Zhang Jianzhong Source: http://blog.csdn.net/zouxy09/article/details/8775518 Deep learning is a new field in the study of machine learning, motivated by the establishment and simulation of neural networks that analyze and learn like the human brain. It mimics the mechanisms of the human brain to interpret data such as images, sounds, and text. Deep learning is a … Read more
(Click the public account above, you can quickly follow) Source: Poll’s Notes cnblogs.com/maybe2030/p/5597716.html If you have good articles to submit, please click → here for details Table of Contents 1. Neuron Model 2. Perceptron and Neural Networks 3. Backpropagation Algorithm 4. Common Neural Network Models 5. Deep Learning 6. References Currently, deep learning (Deep Learning, … Read more
Reprinted from Yunqi Community as required ID: yunqiinsight Author | doflamingo Introduction I have touched on deep learning during my studies, but only superficially. In this era of data and algorithms, it is necessary to get closer to the algorithms. Therefore, from the perspective of an engineer, I hope to record the basics of deep … Read more
Original link:https://medium.com/towards-artificial-intelligence/one-lego-at-a-time-explaining-the-math-of-how-neural-networks-learn-with-implementation-from-scratch-39144a1cf80 From:Yongyu Excerpted from Algorithm Notes https://github.com/omar-florez/scratch_mlp/ The author explains step by step the mathematical processes used in training a neural network from scratch. Neural networks are cleverly arranged linear and nonlinear modules. The above image describes some of the mathematical processes involved in training a neural network. We will explain this in the … Read more
(Click the public account above to follow quickly) Source: CodeMeals cnblogs.com/fengfenggirl/p/bp_network.html If you have good articles to submit, please click → here for details Neural networks were once very popular, went through a period of decline, and are now gaining popularity again due to deep learning. There are many types of neural networks: feedforward networks, … Read more
▌1. Introduction: As a beginner in AI, I referenced some articles and wanted to take some notes to deepen my understanding. I am sharing this for those who need it, and I hope it helps others as well! [Toxic Chicken Soup]: Algorithms often leave you in a state of confusion –> “Who am I, where … Read more
Source: CodeMeals cnblogs.com/fengfenggirl/p/bp_network.html Neural networks were once very popular, went through a period of decline, and are now gaining popularity again due to deep learning. There are many types of neural networks: feedforward networks, backpropagation networks, recurrent neural networks, convolutional neural networks, etc. This article introduces the basic backpropagation neural network (BP), focusing on the … Read more
Madio.net Mathematics China ///Editor: Only Tulips’ Garden This article is reproduced from Anli University Mathematical Modeling. D1 Algorithm Introduction The Artificial Neural Networks (ANN) system emerged in the 1940s. It consists of numerous neurons connected by adjustable weights, featuring large-scale parallel processing, distributed information storage, and strong self-organizing and self-learning capabilities. The BP (Back Propagation) … Read more