40 skills found · Page 1 of 2
pytorch-tabular / Pytorch TabularA unified framework for Deep Learning Models on tabular data
jrzaurin / Pytorch WidedeepA flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
NVIDIA-Merlin / NVTabularNVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
LAMDA-Tabular / TALENTA comprehensive toolkit and benchmark for tabular data learning, featuring 35+ deep methods, more than 10 classical methods, and 300 diverse tabular datasets.
DataCanvasIO / DeepTablesDeepTables: Deep-learning Toolkit for Tabular data
Qwicen / NodeNeural Oblivious Decision Ensembles for Deep Learning on Tabular Data
yandex-research / Rtdl Revisiting Models(NeurIPS 2021) Revisiting Deep Learning Models for Tabular Data
jainammm / TableNetUnofficial implementation of "TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images"
piyushpathak03 / Recommendation SystemsRecommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems
zhuyitan / IGTDImage Generator for Tabular Data (IGTD): Converting Tabular Data to Images for Deep Learning Using Convolutional Neural Networks
LAMDA-Tabular / Tabular SurveyAwesome Tabular Deep Learning for "Representation Learning for Tabular Data: A Comprehensive Survey"
naity / Image TabularIntegrate image and tabular data for deep learning
AmanSavaria1402 / TableNetTableNet: Deep Learning model for end-to-end Table Detection and Tabular data extraction from Scanned Data Images In modern times, more and more number of people are sharing their documents as photos taken from smartphones. A lot of these documents contain lots of information in one or more tables. These tables often contain very important information and extracting this information from the image is a task of utmost importance. In modern times, information extraction from these tables is done manually, which requires a lot of effort and time and hence is very inefficient. Therefore, having an end-to-end system that given only the document image, can recognize and localize the tabular region and also recognizing the table structure (columns) and then extract the textual information from the tabular region automatically will be of great help since it will make our work easier and much faster. TableNet is just that. It is an end-to-end deep learning model that can localize the tabular region in a document image, understand the table structure and extract text data from it given only the document image. Earlier state-of-the-art deep learning methods took the two problems, that is, table detection and table structure recognition (recognizing rows and columns in the table) as separate and treated them separately. However, given the interdependence of the two tasks, TableNet considers them as two related sub-problems and solves them using a single neural network. Thus, also making it relatively lightweight and less compute intensive solution.
ptuls / Tabnet ModifiedModification of TabNet as suggested in the Medium article, "The Unreasonable Ineffectiveness of Deep Learning on Tabular Data"
lmassaron / Deep Learning For Tabular DataAn updated (2025) guide to Deep Learning for tabular data, comparing a fine-tuned Keras 3 (PyTorch backend) DNN and an Optuna-optimized XGBoost model on the Kaggle Amazon Employee Access Challenge
arnor-sigurdsson / EIRA toolkit for training deep learning models on genotype, tabular, sequence, image, array and binary data.
microsoft / CASPRCASPR is a deep learning framework applying transformer architecture to learn and predict from tabular data at scale.
Apress / Modern Deep Learning Tabular DataSource Code for 'Modern Deep Learning for Tabular Data' by Andre Ye and Ziang Wang
unnir / DeepTLFA Novel Hybrid Deep Learning Model for Heterogeneous Tabular Data
AmirhosseinHonardoust / Teaching Neural Networks To Imagine TablesA comprehensive deep dive into how Variational Autoencoders (VAEs) learn to generate realistic synthetic tabular data. This project explores latent space learning, probabilistic modeling, and neural creativity, combining data privacy, interpretability, and generative AI techniques in a structured format.
