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.

A likely error-prone bottleneck.

This is the Algorithm to detect the Handwritten Digits - Autoencoders

template for Rest API

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

A Keras implementation of YOLOv3 (Tensorflow backend)

This builds a minimalistic memory allocator that can be used to manually manage virtual memory. The goal is to have a reliable library that accounts for explicit allocation, reallocation, and initialization of memory.

[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…

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.

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

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

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

Generate labels for (wine) bottle neck

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.