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1711661_sp2020_cse373_final

The Final Submission of CSE373 Design and Analysis of Algorithms under SAS3 Sir.

andrew-ng-notes

This is Andrew NG Coursera Handwritten Notes.

anomalib

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

awesome-ml-courses

Awesome free machine learning and AI courses with video lectures.

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

cd

This repo contains continuous delivery pipeline with AWS CodeBuild. I've implemented this pipeline with hugo template on aws.

ci

This is a demo repo where I've used the cloud9 to build a python scaffold with makefile, test, lint and used GitHub Actions for continuous integration

clustering_spotify_songs

A cluster is a group of objects that belongs to the same class. In other words, similar objects are grouped in one cluster and dissimilar objects are grouped in another cluster. Methods Used in this -Model-Based Method -Hierarchical Method -Constraint-Based Method -Grid-Based Method -Partitioning Method -Density-Based Method

cml_dvc

comp_353_winter_2023

COMP 353: Database

coursera_courses

Coursera is one of the leading platforms for online courses. There are hundreds of courses you can enroll in and learn. I'm interested in the Data Science field. So I searched and find out some good courses in Coursera taught by some great instructors.

covid-19_sentiment_analysis

[CMMM'2021] Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

cs-video-courses

List of Computer Science courses with video lectures.

cse331l-section-10-fall20-nsu

This is the official Github Repository for CSE331L: Microprocessor Interfacing & Embedded System Lab, Section 10, Fall 2020. All course-related materials and Code submission will be facilitated here.

cse445_machine_learning

This repo contains all of the files, slides, notes for the Machine Learning (CSE445) course. Denoising using an autoencoder is mainly a modification on the network to prevent it from learning the identity function. The autoencoder sometimes becomes so big that it only learns the data and makes the output as input. It doesn’t perform any dimension reduction rather corrupts the input data by adding some noise or mask in the input values.

data-science-question-answer

A repo for data science related questions and answers

developer-roadmap

Roadmap to becoming a web developer in 2018

diminishing_image_noise_using_deep_learning

Denoising an image is a classical problem that researchers are trying to solve for decades. In earlier times, researchers used filters to reduce the noise in the images. They used to work fairly well for images with a reasonable level of noise. However, applying those filters would add a blur to the image. And if the image is too noisy, then the resultant image would be so blurry that most of the critical details in the image are lost. There has to be a better way to solve this problem. As a result, I have implemented several deep learning architectures that far surpass the traditional denoising filters. In this blog, I will explain my approach step-by-step as a case study, starting from the problem formulation to implementing the state-of-the-art deep learning models, and then finally see the results.

djangorestapiml

Using the Iris dataset to predict the flower. This repository focused on creating a rest API for the Iris dataset. Using Django, Django-rest-framework, and Jupiter Notebook to complete this project. To testify the API use Postman.

dlaicourse

Notebooks for learning deep learning

dockerdjangoreactml

With the Django REST system, we can construct a simple machine learning application that forecasts the species of a sample flower based on measurements of its characteristics i.e. the dimensions of the sepal and petal, length, and width. Here we'd use the same Django application and make some adjustments as appropriate. We will use Postgres as our database for this development since Postgres is best suited for building production. Django comes bundled with a fantastic dashboard from the admin. We can register users for our application with the admin dashboard, which can then communicate with our machine learning application to make predictions. This will then serve the function of our backend and admin duties for our Django application.

dukehugofeb1

this is a Hugo continuous delivery site

eb

This repo contains a flask application and deploys it using PaaS (AWS Elastic Beanstalk) and implemented continuous delivery (AWS CodePipeline)

ecr

This repo contains a simple implementation of aws ecr and dockerfile on cloud9.

fedgcn

FedGCN: Communication Tradeoffs in Federated Training of Graph Convolutional Networks

graph-network

Graph Neural Network

hf_nlp

The repo contains all the resource from HuggingFace NLP course

human_ai_gsoc_2024

inse_6220_lab_winter_2023

INSE 6220 (Advanced Statistical Approaches to Quality)