Xu Bing's repositories
awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT better.
TinyWebServer
:fire: Linux下C++轻量级Web服务器学习
deep-learning-for-image-processing
deep learning for image processing including classification and object-detection etc.
DeepLearning
A deep learning code base, mainly for paper replication, in the areas of image recognition, object detection, image segmentation, self-supervision, etc. Each project can be run independently, and there are corresponding articles to explain.
examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
free-programming-books
:books: Freely available programming books
GIS-
基于百度地图API的共享篮球系统
interview
📚 C/C++ 技术面试基础知识总结，包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。This repository is a summary of the basic knowledge of recruiting job seekers and beginners in the direction of C/C++ technology, including language, program library, data structure, algorithm, system, network, link loading library, interview experience, recruitment, recommendation, e
OpenKE
An Open-Source Package for Knowledge Embedding (KE)
pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
Semantic-Textual-Similarity
Abstract Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications of this task include machine translation, summarization, text generation, question answering, short answer grading, semantic search, dialogue and conversational systems. We developed Support Vector Regression model with various features including the similarity scores calculated using alignment-based methods and semantic composition based methods. We have also trained sentence semantic representations with BiLSTM and Convolutional Neural Networks (CNN). The correlations between our system output the human ratings were above 0.8 in the test dataset. Introduction The goal of this task is to measure semantic textual similarity between a given pair of sentences (what they mean rather than whether they look similar syntactically). While making such an assessment is trivial for humans, constructing algorithms and computational models that mimic human level performance represents a difficult and deep natural language understanding (NLU) problem. Example 1: English: Birdie is washing itself in the water basin. English Paraphrase: The bird is bathing in the sink. Similarity Score: 5 ( The two sentences are completely equivalent, as they mean the same thing.) Example 2: English: The young lady enjoys listening to the guitar. English Paraphrase: The woman is playing the violin. Similarity Score: 1 ( The two sentences are not equivalent, but are on the same topic. ) Semantic Textual Similarity (STS) measures the degree of equivalence in the underlying semantics of paired snippets of text. STS differs from both textual entailment and paraphrase detection in that it captures gradations of meaning overlap rather than making binary classifications of particular relationships. While semantic relatedness expresses a graded semantic relationship as well, it is non-specific about the nature of the relationship with contradictory material still being a candidate for a high score (e.g., “night” and “day” are highly related but not particularly similar). The task involves producing real-valued similarity scores for sentence pairs. Performance is measured by the Pearson correlation of machine scores with human judgments.
Train_Custom_Dataset
标注自己的数据集，训练、评估、测试、部署自己的人工智能算法
python-Machine-learning
机器学习算法项目
pytorch-examples
Simple examples to introduce PyTorch
transferlearning-tutorial
《迁移学习简明手册》LaTex源码