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此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
Inpaint anything using Segment Anything and inpainting models.
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
Segment Anything in High Quality [NeurIPS 2023]
小型金融知识图谱构建流程（neo4j / python / cypher / KG）
人工精调的中文对话数据集和一段chatglm的微调代码
tensorflow implementation
AI-powered Text-to-Art Generator - Text2Art.com
[2017知乎看山杯 多标签 文本分类] ye组(第六名) 解题方案
TryOnDiffusion: A Tale of Two UNets Implementation
An Automatic Speech Recognition Frame ，一个中文语音识别的完整框架， 提供了多个模型
SOTA punctation restoration (for e.g. automatic speech recognition) deep learning model based on BERT pre-trained model
Lending Club Loan data analysis
Applying Deep Learning and NLP in Quantitative Trading
Documentation and code for downloading, cleaning, munging, and analyzing financial statements filed by publicly traded companies with the SEC
Predicting Air Pollution in Beijing, China using Deep Neural Nets
基于Pytorch 1.0 实现的中文断句与标点符号恢复。
使用 Keras 进行中文文本分类
Given a sentence, predict if the sentence is a question or not
NLP-10 k report Sentiment Analysis
Faster R-CNN for pedestrian detection
An end-to-end sentiment analysis library, including TextCNN, BERT, Docker, React.js, etc.
Text classification experiments using TextCNNs and Bi-attentive Classification Networks
LendingClub 贷款数据分析，分析平台个人贷款业务特点、贷款客户特征、贷款违约影响因素，并构建贷款违约预测模型。
Using natural language processing and machine learning to predict financial distress from Risk Factors section in 10K filling annual report
Complete data analysis and machine learning models through lending club dataset