Learning to create Machine Learning Algorithms
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Updated
Jun 15, 2021 - Python
Learning to create Machine Learning Algorithms
Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.
Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine
Essential NLP & ML, short & fast pure Python code
GloVe word vector embedding experiments (similar to Word2Vec)
Car Accident Severity Analysis - Seattle Washington (Machine Learning Application)
Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN
Model-Free-Episodic-Control implementation.
knn with cosine similarity (distance)
An implementation of the K-Nearest Neighbors algorithm from scratch using the Python programming language.
Work done at the Recurse Center or with Recursers
Being able to perform gameplay analysis of NBA players, NBA Predictive Analytics is a basketball coach's new best friend.
Spherical k-nearest neighbors interpolation (geospatial interpolator)
This toolbox offers 6 machine learning methods including KNN, SVM, LDA, DT, and etc., which are simpler and easy to implement.
This is the official code for our paper "Simple and Scalable Nearest Neighbor Machine Translation" (ICLR 2023).
An implementation of a density based outlier detection method - the Local Outlier Factor Technique, to find frauds in credit card transactions. For detecting both local and global outliers.
Predicts gender of the voice signal
Codes and images used for blog post at https://www.mdelcueto.com/blog/example-nearest-neighbors-regression/
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