backpropagation

Implementing Convolutional Neural Networks in C++ from Scratch

by
Implementing Convolutional Neural Networks in C++ from Scratch

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

Derivation of Backpropagation Algorithm in Convolutional Neural Networks (CNN)

by
Derivation of Backpropagation Algorithm in Convolutional Neural Networks (CNN)

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

A Brief History of Neural Networks: A Breakthrough After BP Algorithm – Belief Networks

by
A Brief History of Neural Networks: A Breakthrough After BP Algorithm - Belief Networks

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

5 Common Derivatives of Neural Networks with Detailed Formula Process

by
5 Common Derivatives of Neural Networks with Detailed Formula Process

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

by
Principles of Neural Network Algorithms in Deep Learning

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

Understanding Neural Network Training: A Comprehensive Guide

by
Understanding Neural Network Training: A Comprehensive Guide

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

A Step-by-Step Guide to Learning Neural Network Mathematics

by
A Step-by-Step Guide to Learning Neural Network Mathematics

Madio.net Mathematics China ///Editor: Only tulips’ garden Neural networks are a clever combination of linear and nonlinear modules. When we wisely choose and connect them, we have a powerful tool to approximate any mathematical function. For example, using nonlinear decision boundaries for classification. The backpropagation technique is responsible for updating the trainable parameters. Although it … Read more

In-Depth Analysis of Invertible Neural Networks: Making Neural Networks Lighter

by
In-Depth Analysis of Invertible Neural Networks: Making Neural Networks Lighter

Source: PaperWeekly This article is about 4600 words long, and it is recommended to read it in 10 minutes. This article analyzes the reversible residual networks as the basis. Why Use Reversible Networks? Because both encoding and decoding use the same parameters, the model is lightweight. The reversible denoising network InvDN has only 4.2% of … Read more

Understanding Neural Networks, Manifolds, and Topology Through 18 Visuals

by
Understanding Neural Networks, Manifolds, and Topology Through 18 Visuals

So far, a major concern about neural networks is that they are difficult to interpret black boxes. This article primarily explains theoretically why neural networks perform so well in pattern recognition and classification. Essentially, they distort and transform the original input through layers of affine transformations and nonlinear transformations until different categories can be easily … Read more

Master Neural Networks in One Article

by
Master Neural Networks in One Article

Essentially, deep learning is a trendy new term derived from a topic that has existed for quite some time – neural networks. >>>> Since the 1940s, deep learning has developed rapidly, achieving great success and being widely used in smartphones, cars, and many other devices. So, what are neural networks, and what can they do? … Read more