Source: Yunqi Community This article contains 6500 words and is recommended to be read in 10 minutes. Starting from the historical development of neural networks, this article introduces the perceptron model, feedforward neural networks, and the BP algorithm. [Introduction] What comes to mind when you think of neural networks? Have you ever pondered the principles … Read more
Basic Mathematical Principles The main differences in artificial neural network models lie in the topology of the neural network connections, the characteristics of the neurons, and the scale of learning, etc. Based on the differences in the topology of neuron connections, we can roughly categorize neural network models into the following two types. 1. Feedforward … Read more
Source | Network One thing that might surprise beginners is that neural network models are not complex! The term ‘neural network’ sounds impressive, but in fact, neural network algorithms are simpler than people think. This article is entirely prepared for beginners. We will understand the principles of neural networks by implementing one from scratch using … Read more
Table of Contents 1. Neuron Model 2. Perceptron and Neural Networks 3. Error Backpropagation Algorithm 4. Common Neural Network Models 5. Deep Learning 6. References Currently, deep learning (Deep Learning, referred to as DL) is exceptionally popular in the field of algorithms, not only in the internet and artificial intelligence but also in various fields … Read more
Madio.net Mathematics China Reprinted from:Author丨Mountain and Water You and Me @ Zhihu Source丨https://zhuanlan.zhihu.com/p/468100301 Editor丨Extreme City Platform Currently, building a Convolutional Neural Network (CNN) is generally done directly using deep learning frameworks such as Pytorch or Tensorflow, which is quite simple. However, if you’re writing the backpropagation process from scratch, it is much more complicated than … Read more
Author: Nan Ke Yi Meng Ning Chen Lun @ Zhihu (Authorized) Editor: Jishi Platform Source: https://zhuanlan.zhihu.com/p/61898234 The mathematical derivation of backpropagation in multilayer perceptrons is mainly represented using mathematical formulas. In fully connected neural networks, these are not complicated, and even purely mathematical formulas are relatively easy to understand.However, convolutional neural networks are relatively more … Read more
Click the blue text above to follow ↑↑↑↑↑ Author: Andrey Kurenkov As the puzzle of training multi-layer neural networks is unraveled, this topic has once again become extraordinarily popular, and Rosenblatt’s lofty ambitions seem to be realized. Until 1989, another key discovery was published, which is still widely cited in textbooks and major lectures. Multi-layer … Read more
Author: Criss Source: Machine Learning and Generative Adversarial Networks 01 Derivative of Softmax 1.1 Derivative of Softmax Generally, the last layer of a classification model is the softmax layer. Assuming we have a classification problem, the structure of the corresponding softmax layer is shown in the figure below (it is generally considered that the output … Read more
Principles of Neural Network Algorithms in Deep Learning Graphical Parameter Calculation Junior High Mapping Neural Network Mapping? What is a Neural Network Algorithm? Parameter Solving References What is a Neural Network Algorithm? Junior High Mapping In junior high school, we learned about mapping with the equation y = f(x). By using several pairs of values … Read more
In recent years, artificial intelligence has developed rapidly, gradually penetrating various industries and fields. More and more people are learning AI-related technologies. To help beginners quickly grasp the basic principles of AI, Professor Ma Shaoping, Vice Chairman of CAAI, has written an introductory book titled “How Computers Achieve Intelligence.” Through the new popular science column … Read more