datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MachineLearning
Machine Learning Tier
This dataset is a collection of synthetic microlensing light curves from the Nancy Grace Roman Space Telescope Galactic Bulge Time Domain Survey. It is intended for the training and benchmarking of machine learning models for microlensing event classification, parameter estimation, and anomaly detection.
The raw distribution of event properties is not representative of what Roman will see, but should span a statistically larger set of events. More… See the full description on the dataset page: https://huggingface.co/datasets/RGES-PIT/MachineLearning.quantum-machine-learning-theory
Neura Parse — Quantum Machine Learning Theory: Trainability, Generalization & Learning From Quantum Data
A research-depth, proof-oriented vertical on the learning theory of quantum models and quantum data. Covers why parameterized quantum circuits train or don't (barren plateaus), what they can represent, when they generalize or provably beat classical models, and — for quantum data — how to predict properties of unknown states/channels with few measurements (classical… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-machine-learning-theory.quantum-machine-learninga continuous data scrape of arxiv and google scholar papers of quantum machine learning papers particularly regarding climate.
SO-Python_QA-Data_Science_and_Machine_Learning_classquantum-machine-learning-models
Neura Parse — Quantum Machine Learning Models: Encodings, Kernels, QNNs & Generative/Deep Architectures
A hands-on, code-first vertical on quantum models that learn from data. Spans data encodings/feature maps, variational classifiers, quantum kernels/QSVMs, and quantum neural networks through modern generative and deep architectures (quantum GANs, circuit Born machines, quantum Boltzmann machines, QCNNs, quantum autoencoders, quantum RL, and quantum… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-machine-learning-models.machine-failure-logsMachine-Learning-Credit-Card-Fraud-Detection-Projectmachine_learning_projectThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"robot_type": "so-100",
"codebase_version": "v3.0",
"total_episodes": 27,
"total_frames": 18800,
"total_tasks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:27"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"features": {… See the full description on the dataset page: https://huggingface.co/datasets/Delcastillo8/machine_learning_project.Titanic-Machine-Learning-from-Disaster-0.77751aminasalamt_heart-disease-prediction-using-machine-learning
Kaggle Top 1000 Small Datasets
Collection of 1000 high-quality small datasets from Kaggle (~98MB).
Usage
from huggingface_hub import snapshot_download
import pandas as pd
path = snapshot_download(repo_id="jason1966/aminasalamt_heart-disease-prediction-using-machine-learning", repo_type="dataset")
df = pd.read_csv(f"{path}/datasets/author_dataset/file.csv")
See metadata.json for full dataset info.
machine-learning-forecasting-dataBiogas-Production-Machine-Learning-Analysismachine_learningMachine-Learning-Car-Price-Prediction-ProjectdatasetsMolecular-machine-learning-project
MML Final Project: Polymer Property Prediction Artifacts
Team PolyPredictors
This dataset contains the model weights and imputed datasets for our final project benchmarking AIMNet-X2D and Mamba.
Contents
models/: AIMNET folder for AIMNET-X2D, MAMBA folder for MAMBA (Model except multi_5props_best.pthwas trained for predict sparse dataset).
results/:
multi_5props_predictions.csv: Final predictions for the test set.
*filled_nan.csv: Training datasets with missing values… See the full description on the dataset page: https://huggingface.co/datasets/lck0328/Molecular-machine-learning-project.