Md. Belal Hossain's repositories
COVID19-ResNet50-TL
Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images
pet-projects
The list of my pet projects (personal projects) can be found here.
certifications
My online licenses & certifications can be found here.
simplefolio
⚡️ A minimal portfolio template for Developers
newt
Natural World Tasks
BenchmarkTransferLearning
Official PyTorch Implementation and Pre-trained Models for Benchmarking Transfer Learning for Medical Image Analysis
concrete-crack-detection
Concrete Crack Detection - PyTorch Transfer Learning with Data Augmentation
crack-detection
Crack Detection using ML
django-proxy-user
Example of django proxy model in custom user model
ssl-transfer
Official code for the CVPR 2021 paper "How Well Do Self-Supervised Models Transfer?"
coding-interview-university
A complete computer science study plan to become a software engineer.
interview
Interview questions
django-youtube-api
Django application with YouTube-API
Using-Python-for-Research
Using Python for Research is a course on edx.org. I am a student of this course.
django-bkash-webhook-example
Django bKash Webhook example
api-samples
Code samples for YouTube APIs, including the YouTube Data API, YouTube Analytics API, and YouTube Live Streaming API. The repo contains language-specific directories that contain the samples.
django-starter
A Django Starter project for easy to start Django web application.
django-youtube
Youtube API wrapper app for Django
inat_comp
iNaturalist competition details
competitive-programming
Problem solving and competitive programming.
crack_segmentation
This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu
django-phone-verify
A Django app to support phone number verification using security code / One-Time-Password (OTP) sent via SMS.
django-mptt-example
This is a simple example of django-mptt.
Data-Structures-and-Algorithms
This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.
it-cert-automation-practice
Google IT Automation with Python Professional Certificate - Practice files
google-it-automation-github
This repository is for "Qwiklabs Assessment: Introduction to Github" of "Google IT Automation with Python" SPECIALIZATION
computer-networks
Computer Networks fundamentals using Python.
coursera-deep-learning-specialization
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models