Tabular Data
Showing 21 of 21 projects
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
A GPU-accelerated DataFrame library for tabular data processing, part of the RAPIDS data science suite.
A terminal spreadsheet multitool for exploring and arranging tabular data from various formats.
A high-performance Python DataFrame library for lazy out-of-core processing and visualization of billion-row datasets at interactive speeds.
A Python library for handling tabular datasets across multiple formats like XLS, CSV, JSON, and YAML.
An open-source solution for continuous validation of machine learning models and data, from research to production.
A blazing-fast command-line toolkit for querying, slicing, analyzing, transforming, and validating tabular data (CSV, Excel, JSONL, etc.).
A Go library providing DataFrames, Series, and data wrangling operations for structured data manipulation.
A Go library providing DataFrames, Series, and data wrangling operations for tabular data manipulation.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
Ajax-enabled jQuery grid plugin for displaying and manipulating tabular data in web applications.
Automatic neural architecture search and hyperparameter optimization for PyTorch, focusing on tabular data and time series forecasting.
A fast, header-only C++11 library for reading CSV files with automatic column rearrangement, threading for I/O overlap, and configurable parsing features.
A set of Backbone.js components for building semantic, easily stylable data grid widgets with a clean JavaScript API.
A flexible and fast package for in-memory tabular data manipulation and analysis in the Julia programming language.
An R package for creating, modifying, analyzing, and visualizing network graphs from tabular data.
A unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
A feature-rich Angular component for displaying and managing tabular data with built-in CRUD operations.
A Python package for generating synthetic tabular and time-series data using state-of-the-art generative models like GANs and Gaussian Mixtures.
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