An implementation of trigger word detection using keras, for detecting digits spoken in an audio

Precision 과 Recall 에 대한 이해를 돕고자 작성한 자료입니다.

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses. The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse th…

C++ math library for tracking significant digits during calculations.

Python module providing an easy way to set the precision of a floating-point number to the desired amount of decimal places, or total amount of significant digits.

Comparing the performance of MLP (multilayer perceptron) and CNN (convolutional neural network) on USPS dataset and visualizing it via TensorBoard.

An attempt at the network anomaly detection task using manually implemented k-means, spectral clustering and DBSCAN algorithms, with manually implemented evaluation metrics (precision, recall, f1-score and conditional entropy) used to evaluate these algorithms.

Build repository for brambox - https://gitlab.com/eavise/brambox

Better multi-class confusion matrix plots for Scikit-Learn, incorporating per-class and overall evaluation measures.

Natural logarithm of 2.

📚 Tore epitech maths project. Continuation of the 101pong, and should allow to draw more complex forms, such as tores (2th and 4th degree equation (in the case of tore))

A Comprehensive Guide to Titanic Machine Learning from Disaster

Another Big Numbers Library

This project is about detecting fraudulent credit card transactions. The dataset tends to be highly imbalanced, with less than 0.2% of the observations labelled as fraudulent. To address this issue we have to take into account the bank's objective (maximizing precision or recall) and restrictions. The performance and efficiency of many classific…

my implementation calculate precision, recall, f1, accuracy, and GLEU score

Explore direct neighbors and limits of IEEE floating-point values.

Handy reference notes for common data sciences topics

Evaluation of the performance of classification models can be facilitated through a combination of calculating certain types of performance metrics and generating model performance evaluation graphics. The purpose of this exercise is to calculate a suite of classification model performance metrics via Python code functions.

Small little aim trainer made from python.

Implementation of cross-calibration and precision analysis for HR-pQCT