Machine Learning

Evaluating the Safety and Trustworthiness of Generative AI Models

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Evaluating the Safety and Trustworthiness of Generative AI Models

In recent years, generative artificial intelligence technology has made significant advancements. With various large models continuously iterating and upgrading, their capabilities have improved significantly, from general generative abilities to specialized capabilities in various domains, and now with a greater focus on actual user interaction. The applications of artificial intelligence are increasingly gaining attention. However, current … Read more

MatterGen: A New Paradigm in Material Design Based on Generative AI

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MatterGen: A New Paradigm in Material Design Based on Generative AI

(Estimated reading time: 8 minutes) Editor’s note: Recently, the Microsoft Research Center for Scientific Intelligence proposed an innovative generative AI material design tool, MatterGen, which breaks through the limitations of traditional material screening methods. It can efficiently explore a broader material space and directly generate new materials based on application requirements. The model can optimize … Read more

Understanding Convolutional Neural Networks (CNN)

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Understanding Convolutional Neural Networks (CNN)

Brothers, today we are going to talk about a particularly “high-end” but actually very practical and interesting technology – Convolutional Neural Networks (CNN). Don’t be afraid, the name sounds quite intimidating, but actually, if we chat in plain language, you will find that, hey, this thing is not that difficult! First, we need to understand … Read more

Analyzing AlexNet: General Structure of CNN

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Analyzing AlexNet: General Structure of CNN

Author: Zhang Xu Editor: Wang Shuwei This article has 4794 words and 27 images, reading time is about 11 minutes Forget it Read as long as you want Zero Reference Directory: 1. Convolutional Layer 1.1 The Role of Convolutional Layer in CNN 1.2 How Convolutional Layers Work 1.3 Convolutional Layers in AlexNet 2. Pooling and … Read more

Exploring 1D CNN for Kaggle Competitions

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Exploring 1D CNN for Kaggle Competitions

↑↑↑ Follow after Star ”Alchemy Notes Alchemy Notes Author: Jie Shao, Guest of Alchemy Notes 1DCNN Introduction Recently, many data competitions on Kaggle have featured the 1DCNN model. After reviewing related competitions, I found the concept of 1DCNN to be quite interesting. The basic idea is: First, we map the original features to a high-dimensional … Read more

Building CNN Networks with Object-Oriented Programming | PyTorch Series

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Building CNN Networks with Object-Oriented Programming | PyTorch Series

Click the “Beginner’s Visual Learning” above to choose to add “Star” or “Pinned“. Important content delivered promptly. From a high-level perspective of our deep learning project, we have prepared the data, and now we are ready to build our model. Prepare Data Build Model Train Model Analyze Model Results When we talk about the model, … Read more

Evolution of CNN Architectures: From LeNet to DenseNet

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Evolution of CNN Architectures: From LeNet to DenseNet

Editor | Fish Da Reprinted from | Champion’s Trial Blog Total words:21041 Images:18 Estimated reading time: 53 minutes Convolutional Neural Networks (CNNs) have become a prominent framework in the field of deep learning, especially excelling in computer vision. Starting from LeNet in the 1990s, CNNs went through a decade of silence until AlexNet revived them … Read more

Bold and Innovative Neural Network Structures in CNN

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Bold and Innovative Neural Network Structures in CNN

Click the above “AI Youdao” and select “Star” public account Heavyweight content delivered immediately Editor: Yi Zhen https://www.zhihu.com/question/337470480 This article is for academic sharing only. If there is an infringement, it will be deleted. Reports on machine learning algorithms and natural language processing What Bold and Innovative Neural Network Structures Exist in Convolutional Neural Networks? … Read more

Understanding the Mathematical Principles Behind RNNs

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Understanding the Mathematical Principles Behind RNNs

0Introduction Nowadays, discussions about machine learning, deep learning, and artificial neural networks are becoming more and more prevalent. However, programmers often just want to use these magical frameworks without wanting to know how they actually work behind the scenes. But if we could grasp these underlying principles, wouldn’t it be better for us to use … Read more