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RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
This program will approximate the Traveling salesman problem using 3 three different algorithms (Nearest Neighbot, 2Opt, and 3Opt). There are 6 different combinations and each can be run individually or in suite as part of a benchmark test.
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
Building a KNN model. The dataset for this project collects part of the knowledge from the API TMDB, which contains only 5000 movies out of the total number. Model the data using a KNN, analyze the results and optimize the model.