Apress / modern-deep-learning-tabular-data
Source Code for 'Modern Deep Learning for Tabular Data' by Andre Ye and Ziang Wang
β30Updated 2 years ago
Alternatives and similar repositories for modern-deep-learning-tabular-data:
Users that are interested in modern-deep-learning-tabular-data are comparing it to the libraries listed below
- A collection of companion Jupyter notebooks for Ensemble Methods for Machine Learning (Manning, 2023)β77Updated last year
- Automatic machine learning for tabular data. β‘π₯β‘β69Updated 3 years ago
- Feature engineering package with sklearn like functionalityβ51Updated 4 months ago
- β113Updated 11 months ago
- nbsynthetic is simple and robust tabular synthetic data generation library for small and medium size datasetsβ63Updated last year
- Repository for the explanation method Calibrated Explanations (CE)β60Updated this week
- Code for the new Manning book on machine learning on tabular datasetsβ14Updated last month
- β11Updated last year
- Official repository for the book Time Series Forecasting with Foundation Modelsβ13Updated this week
- Tutorial on time-series forcasting with scikit-learnβ35Updated last year
- ML Assistant for Competitive Machine Learningβ99Updated this week
- β32Updated last year
- A presention of core concepts and a data generator making easier using tabular data with TensorFlow and Kerasβ41Updated last year
- Resources for some of our education contentβ35Updated this week
- A python library to automate feature selection process for machine learning projects.β56Updated last year
- SurvivalGAN: Generating Time-to-Event Data for Survival Analysisβ27Updated last year
- β49Updated 2 years ago
- β23Updated 3 months ago
- How to Interpret SHAP Analyses: A Non-Technical Guideβ51Updated 3 years ago
- Code for the AISTATS 2024 Paper "From Data Imputation to Data Cleaning - Automated Cleaning of Tabular Data Improves Downstream Predictivβ¦β20Updated 11 months ago
- Package designed for handling imbalanced classificationβ18Updated last year
- Automatically transform all categorical, date-time, NLP variables to numeric in a single line of code for any data set any size.β64Updated this week
- Winning solution of the Kaggle "Google Brain - Ventilator Pressure Prediction" competitionβ10Updated 3 years ago
- Accelerate Deep Learning Workloads with Amazon SageMaker, published by Packtβ16Updated last year
- Tool to set the thresholds for classification problems and to visualize the implications of potential thresholds.β26Updated 3 months ago
- A python Library for Intermittent Demand Methods: Croston, SBA, SBJ, TSB, HES, LES and SESβ32Updated 11 months ago
- Multi-class probabilistic classification using inductive and cross VennβAbers predictorsβ43Updated 2 years ago
- β18Updated 2 years ago
- GluonTS Implementation of Intermittent Demand Forecasting with Deep Renewal Processes arXiv:1911.10416v1 [cs.LG]β32Updated 3 years ago
- Predict the poverty of households in Costa Rica using automated feature engineering.β23Updated 4 years ago