There are 16 repositories under tabular-data topic.
React components for efficiently rendering large lists and tabular data
A desktop application for viewing and analyzing tabular data
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台,我们欢迎开源爱好者发起任何有意义的pr!
Automatic architecture search and hyperparameter optimization for PyTorch
AI code-writing assistant that understands data content
Algorithms for outlier, adversarial and drift detection
📺(tv) Tidy Viewer is a cross-platform CLI csv pretty printer that uses column styling to maximize viewer enjoyment.
Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains
In-memory tabular data in Julia
What's in your data? Extract schema, statistics and entities from datasets
A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
A standard framework for modelling Deep Learning Models for tabular data
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
Machine Learning University: Accelerated Tabular Data Class
Research on Tabular Deep Learning: Papers & Packages
Implementation of TabTransformer, attention network for tabular data, in Pytorch
DeepTables: Deep-learning Toolkit for Tabular data
🚜 Parse text and tables from PDF files.
Multimodal model for text and tabular data with HuggingFace transformers as building block for text data
We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.