This article provides a detailed overview and explanation of popular Graph Neural Networks (GNNs) and their mathematical nuances. The underlying idea of graph deep learning is to learn the structural and spatial features of graphs with nodes and edges, where these nodes and edges represent entities and their interactions. >>> Original Link: https://rish-16.github.io/posts/gnn-math/ Graph Deep … Read more
Click on the above “Mechanical and Electronic Engineering Technology” to follow us When processing images or other spatially structured data, Convolutional Neural Networks (CNN) are a commonly used deep learning model. The design inspiration of CNN comes from the visual processing method of the human brain. Unlike traditional fully connected neural networks, CNN extracts local … Read more
Click the above“Beginner’s Visual Learning” to select “Star” or “Pin” Important Insights Delivered Instantly Official Account: You and Your House Author: Yu Yu Lu Ming Editor: Peter Hello everyone, I am Peter~ There are many doubts about the number of hidden layers and neurons in neural networks. I just saw an article that answers these … Read more
As a programmer, we are accustomed to understanding the underlying principles of the tools and middleware we use. This article aims to help everyone understand the underlying mechanisms of AI models, making it easier for those without an AI background to learn or apply various large models. 1. The Relationship Between GPT and Neural Networks … Read more
Click the above “Little White Learns Vision“, choose to add “Starred” or “Top“ Heavyweight content delivered first-hand The optimization of machine learning (objective) can be simply described as: searching for a set of parameters w for the model that can significantly reduce the cost function J(w). This cost function usually includes performance evaluation over the … Read more
Artificial neural networks are actually like a complex calculator; you input something, and it gives you a result. Just like when you input 2+2 into a calculator, it outputs 4, but an artificial neural network can handle not just simple arithmetic; it can process more complex things like images, text, etc. So, when we say … Read more
Click on the top "Xiaobai Learns Vision", select to add "Star" or "Pin" Heavyweight content delivered at the first time Introduction RNN (Recurrent Neural Network) is an artificial neural network with nodes oriented in a circular connection. Unlike feedforward neural networks, RNNs can utilize internal memory to process any sequential input series, meaning they learn … Read more
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
Introduction: Here comes the valuable content! Udacity Machine Learning course mentor Walker is here to teach you how to understand neural networks in a simple, vivid, and interesting way! What is a neural network? A neural network is a series of simple nodes that, when combined simply, express a complex function. Let’s explain each one … Read more
5.4 The Intellectual The Intellectual Image Source: Freepik ● ● ● Written by｜Zhang Tianrong As physicist and Manhattan Project leader Oppenheimer said, “We are not only scientists, we are also human beings.” Where there are people, there is a world of conflicts, and the scientific community is no exception. People often say, “Science knows no … Read more