gabriellavinco

gabriellavinco's Stars

• harshjuly12/Enhancing-Explainability-in-Fake-News-Detection-A-SHAP-Based-Approach-for-Bidirectional-LSTM-Models

  Enhancing Explainability in Fake News Detection uses SHAP and BiLSTM models to improve the transparency and interpretability of detecting fake news, providing insights into the model's decision-making process.

  Language:Jupyter Notebook122

• Jithsaavvy/Explaining-deep-learning-models-for-detecting-anomalies-in-time-series-data-RnD-project

  This research work focuses on comparing the existing approaches to explain the decisions of models trained using time-series data and proposing the best-fit method that generates explanations for a deep neural network. The proposed approach is used specifically for explaining LSTM networks for anomaly detection task in time-series data (satellite telemetry data).

  Language:Jupyter Notebook245

• mobilise-d/mobgap

  The Mobilise-D algorithm toolbox - Implemented in Python

  Language:Python334

• SamMorton123/26.2-app

  Simple Streamlit app to showcase my track and field Elo ratings.

  Language:Python1

• CMU-MBL/CMU_PanopticDataset_2.0

  Language:Python114

• davewhipps/l-avated

  An RGB-D, visual and lidar image pair dataset of walking terrain.

  Language:Swift3

• jasmcaus/opencv-course

  Learn OpenCV in 4 Hours - Code used in my Python and OpenCV course on freeCodeCamp.

  Language:Python1.1k967

• gkrhines/xThreatR

  An implementation of Karun Singh's Expected Threat with R. Data in the SPADL form is required.

  Language:R224

• Friends-of-Tracking-Data-FoTD/LaurieOnTracking

  Laurie's code for Metrica tracking data.

  Language:Python300100

• metrica-sports/sample-data

  Metrica Sports sample tracking and event data

  37897

• statsbomb/open-data

  Free football data from StatsBomb

  2.5k780