Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
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Updated
Aug 31, 2021 - Python
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
🍊 📦 Frequent itemsets and association rules mining for Orange 3.
Code for "Unsupervised Adaptation for Deep Stereo" - ICCV17
Magnitude of the Effect - An Effect Size and CI calculator
Implementation of the Apriori algorithm in python, to generate frequent itemsets and association rules. Experimentation with different values of confidence and support values.
Deep Neural Networks for Call Of Duty Modern Warfare 2019
Generate a Work Breakdown Structure (WBS) report from a markdown file. A tool that improves software development estimates.
[DEPRECATED] Configuration resolver using confidence and shortstop.
🧠 A simple, fully documented neural network library created for educational purposes, heavily inspired by the `ai` package.
Supplementary material to reproduce "The Unreasonable Effectiveness of Deep Evidential Regression"
A rule-based classification approach called Associative Classification (AC) normally constructs accurate classifiers from supervised learning data sets in data mining
A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets
I implemented the reinforcement learning based model Upper Confidence Bound in both Python and R
My Gateway to crack DSA
An open source initiative to help students prepare for HR interviews.
How to structure technical presentations and deliver them with confidence
Decoding the confidence dataset via SVM, RF, and RNN models.
A suite of tests to assess attention faithfulness for explainability
Market Basket Analysis
Par Inc., is a major manufacturer of golf equipment. It believes that it can increase its market share by introducing a new more durable golf ball with cut-resistant coating The tests with the coating of the ball has been promising and now it wants to see the effect of new coating on driving distance.
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