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findinghiggs_acceleratedml_rapids

Detecting the Higgs boson using accelerated machine learning. The purpose of this notebook is to explore the feasibility of using accelerated machine learning algorithms to detect the Higgs boson particle using data from the Large Hadron Collider. Three different machine learning algorithms are compared and evaluated: SVM, random forest, and XGBoost. Hyperparameter tuning is performed to optimize the models.

objectdetection_tfserving_docker_flask

Object Detection using Flask, TensorFlow Serving and Docker. Implements a web app to which images can be uploaded. A TensorFlow model is then used to perform object detection. The model is served through TensorFlow Serving. The web app and the TF-Serving server is packaged using Docker. Docker-compose is used to create a virtual network for both container such that the Flask app can access the TF Serving container (in this case via REST).

reddit-sentiment-reinforcement-learning

Stock trading using Reddit sentiment data and reinforcement learning.

redditnews

soundclassification_multi_input_network_tf

Environmental sound classification using a multi-input network. This work studies the feasibility of a multi-input network for environmental sound classification. It uses a dataset comprised of sound excerpts of urban environments with 10 classes.

textemplates

twitchdownloader

Twitch VOD/Clip Downloader - Chat Download/Render/Replay