(Deprecated) Scikit-learn integration package for Apache Spark
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Updated
Dec 3, 2019 - Python
(Deprecated) Scikit-learn integration package for Apache Spark
Hyperparameter tuning for machine learning models using a distributed genetic algorithm
Bayesian Optimization and Grid Search for xgboost/lightgbm
All codes, both created and optimized for best results from the SuperDataScience Course
A PyTorch Based Deep Learning Quick Develop Framework. One-Stop for train/predict/server/demo
python experiment management toolset
Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets
Udacity Machine Learning Course Predicting Boston Housing Prices
Codes and images used in post at Towards Data Science: https://towardsdatascience.com/grid-search-in-python-from-scratch-hyperparameter-tuning-3cca8443727b
Case Studies for using Accera - the open source cross-platform compiler from Microsoft Research - to create high performance deep learning computations (i.e. GEMM, Convolution, etc.)
Add the Grid Search functionality to search for optimal hyperparameters while fine-tuning the model. Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images).
Pipeline meant to segment and classify organoids, or any other blob-like structures (star-convex polygons). Microscopy images can be easily annotated in QuPath and automatically processed afterwards to count the class distribution within each image using this pipeline (TIF files will be converted to grayscale)
A Machine Learning project on Identifying Abnormal driving behavior using Spatio-Temporal analysis
Milwaukee Bucks Game Outcome Prediction using Tensorflow
Grid and Graph Search with the A* algorithm (path+cost function) for a drone in an urban environment + Path optimization
Drone control algorithm: A* obstacle avoidance, grid search and image recognition (OpenCV)
Built for the implementation of Keras in Tensorflow. Behaves similarly to GridSearchCV and RandomizedSearchCV in Sci-Kit learn, but allows for progress to be saved between folds and for fitting and scoring folds in parallel.
This project focuses on analyzing patient feedback regarding the treatment provided by home healthcare service agencies.
Hands-on tuning made easy, experiment freely—see what clicks.
This project aims to develop a machine learning model to classify SMS messages as spam or not spam. The project encompasses the entire pipeline from data collection and preprocessing to model training, evaluation, and deployment using Streamlit for an interactive user interface.
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