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This project contains codes and notebooks related to various use cases for banking.
A Tutorial for Agent Based Models in Python
NeurIPS'22 | TransTab: Learning Transferable Tabular Transformers Across Tables
本赛题旨在运用有效的金融科技和大数据系统,分析涉赌涉诈资金交易新方式,持续优化风险监测模型,通过赛题提供的涉赌涉诈黑名单、白名单及用于训练的相关交易流水数据集,构建涉赌涉诈账户算法识别模型,全面排查存量风险。A榜排名11/1594,B榜排名13/1594。
A game theoretic approach to explain the output of any machine learning model.
企业投资价值评估第六名,团队名:xyr
NLP超强入门指南,包括各任务sota模型汇总(文本分类、文本匹配、序列标注、文本生成、语言模型),以及代码、技巧
FinRL: Financial Reinforcement Learning. 🔥
ICAIF 2021 Paper "A Machine Learning Approach to Detect Early Signs of Startup Success"
For trading. Please star.
润学全球官方指定GITHUB,整理润学宗旨、纲领、理论和各类润之实例;解决为什么润,润去哪里,怎么润三大问题; 并成为新中国人的核心宗教,核心信念。
Kaggle competition
This is the official implementation of the paper Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images.
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Advers…
Restricted Boltzmann Machines (RBMs) in PyTorch
The aim of this repository is to create RBMs, EBMs and DBNs in generalized manner, so as to allow modification and variation in model types.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
optic disc segmentation helps to detect various diseases and help doctors to get good idea about the diseases
Optic Disc and Optic Cup Segmentation using 57 layered deep convolutional neural network
This code is used for joint optic disc and cup segmentation from retinal fundus images
A Keras implementation of Optic Disc Segmentation
Code for TMI 2018 "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation"
Multimodal Compact Bilinear Pooling class in Python
A pytorch implementation of bilinear CNN for fine-grained image recognition