Today, I’m excited to introduce you to a super cool Python library—agi-pack. This library is powerful and can help us easily tackle many complex tasks, like a “superhero” in the Python world, making programming simple and fun. Next, let’s dive into the world of agi-pack and embark on a wonderful programming journey! 1. Getting to … Read more
Introduction The development of large language models (LLMs) is advancing rapidly, with each significant update potentially bringing substantial performance improvements and expanding application scenarios. Against this backdrop, Alibaba’s latest release of the Qwen2.5 series models has garnered widespread attention. This technical report provides a detailed overview of the development process, innovations, and performance of Qwen2.5, … Read more
I previously wrote two articles on address processing, detailed below: Address Processing in Risk Control: Simple and Effective Regex Replacement Address Processing in Risk Control: Simple, Accurate, and Effective Multi-label Clustering Today we are writing the third article, using KNN to construct similar addresses. Although this article is about addresses, many scenarios can draw on … Read more
What is the K-Nearest Neighbors Algorithm (KNN)? The K-Nearest Neighbors algorithm (KNN) is a simple and intuitive machine learning algorithm widely used for classification and regression tasks. Its core idea is based on the principle of “birds of a feather flock together,” finding the K most similar neighbors by comparing the distance between a new … Read more
“Birds of a feather flock together” is a common phenomenon in real life, indicating that similar things are likely to have similar attributes. Using this idea, machine learning can classify data, assigning it to the same category, such as similar or “nearest” neighbors. Today, let’s learn about nearest neighbor classification. 1. Understanding Nearest Neighbor Classification … Read more
Research Background In the era of big data and artificial intelligence, the position of literature as a core knowledge carrier has not changed. However, the current methods of literature resource management, such as information retrieval, databases, subject headings, and classification systems, cannot meet the higher-level needs of researchers. The knowledge graph is a novel mass … Read more
1. From Confusion to Curiosity: First Impressions of LangGraph In this era deeply permeated by large model technology, while we enjoy the many conveniences it brings, we are also faced with a series of thorny issues. Take my daily work with large models, for instance; they indeed perform excellently in handling simple tasks, such as … Read more
Notes from Andrew Ng’s DeepLearning.ai Course 【Andrew Ng’s DeepLearning.ai Notes 1】Intuitive Explanation of Logistic Regression 【Andrew Ng’s DeepLearning.ai Notes 2】Simple Explanation of Neural Networks (Part 1) 【Andrew Ng’s DeepLearning.ai Notes 2】Simple Explanation of Neural Networks (Part 2) Having trouble with deep networks? Andrew Ng helps you optimize neural networks (1) 【DeepLearning.ai】Deep Learning: Optimizing Neural Networks … Read more
After a year of development, domestic AI has gradually matured. Today, let’s look at how people can combine the domestic star product Zhipu AI with the LlamaIndex data framework to handle daily tasks. Domestic AI Products As we all know, the AI and large model field has been extremely hot this past year. In August, … Read more
The full name of RAG is Retrieval Augmented Generation, which means “retrieval enhanced generation”. LLMs are trained on a vast amount of data, but this training data does not include your data. RAG solves this problem by adding your data to the data that the LLM already has access to. In RAG, your data is … Read more