A binary+library to measure how much time is spent reading vs writing.

Logs times of page creations and intermediate results to spot bottlenecks in Islandora stack.

single-file "bottle.py" , a website-application µ framework

A small experiment with convolutional neural network in keras.

Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset.

A likely error-prone bottleneck.

This is the Algorithm to detect the Handwritten Digits - Autoencoders

Term 2 Project 1 Dog Breed Classifier and human face detector using ImageNet, superhuman CNNs, and Haar Cascades

A stopwatch extension for phpunit. Get timing for parts of your code to detect performance bottlenecks.

template for Rest API

A Keras implementation of YOLOv3 (Tensorflow backend)

[This project was completed in September 2020] The GML-Net is a convolutional neural network (CNN) that is based on U-Net architecture with an encoder derived from the ResNet family and BottleNeck blocks that provide reading and aggregation of feature maps from a cross-section of various scales. Effective network learning is ensured by loss func…

🔧 Performance Optimization Project - Simulated real-world scenario where a Desktop VR application must be optimized for release

3 part project: A. bottleneck autoencoder, B. manhattan distance, C. earth mover's distance

Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset

Autoencoders are mostly used for different purposes such as denoising, compression data, anomaly detection, generating new data from the input data entering to the model, and more. This repository introduces a simple autoencoder architecture with some brief explanations of encoder, bottleneck and decoder parts.

Generate labels for (wine) bottle neck

Zabbix Graphs Bottleneck Classification automates bottleneck analysis in network infrastructure using deep learning and the Zabbix monitoring system. It quickly identifies and classifies bottlenecks, enabling proactive network management and optimization.