center loss for face recognition
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Updated
Nov 11, 2020 - Python
center loss for face recognition
PyTorch-Based Evaluation Tool for Co-Saliency Detection
Measure and visualize machine learning model performance without the usual boilerplate.
Detect and classify fraudulent transactions using SQL and Python. Generate behavioral features with SQLite, train a Logistic Regression model, and evaluate performance with AUC, precision, recall, and ROC analysis. A complete supervised fraud detection workflow.
Get an intuitive sense for the ROC curve and other binary classification metrics with interactive visualization.
Machine learning utility functions and classes.
Scikit-learn tutorial for beginniers. How to perform classification, regression. How to measure machine learning model performacne acuuracy, presiccion, recall, ROC.
Survival prediction using Titanic dataset and Logistic Regression
Tool demonstrating building credit risk models
calculate ROC curve and find threshold for given accuracy
Classification of spondylodiscitidis vs metastasis in the spine using Neural Networks
A simple neural network with backpropagation used to recognize ASCII coded characters
Predicting the volcanic eruption using ML Algorithms.
An end-to-end Machine Learning web app that visualizes actual data vs future predictions using Logistic Regression, all wrapped in an interactive Streamlit dashboard.
One Data Set with All Algorithms
Advanced Machine Learning
EEG-based brain signal classification using classical machine learning with feature engineering and comparative model evaluation for BCI and NeuroAI research.
This project used machine learning to understand characteristics of terrorist groups that engage in suicide bombings.
__CourseWork__
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