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adaboost

Boosting (originally called hypothesis boosting) refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor

beststackweb

Fullstack App With TypeScript, PostgreSQL, Next.js, Prisma & GraphQL: Data Modeling

breast-cancer-risk-prediction-

Breast cancer risks can be reduced via early detection of the disease; according to the American Cancer Society (2007) early detection of breast cancer risks can help reduce the possibility of mitigating the full growth of tumors

cat-dog-cnn-classifier

This classifier use Convolution Neural Network approch for kaggle problem to classify Cat vs Dog images.

classify_cat_dog

This is a project about creating a flutter application that uses a pre-trained AI data model to do classification between cat and dog with the help of TensorFlow.

dotnetcore-buildpack

Heroku .NET Core Buildpack

emotion-detection

Real-time Facial Emotion Detection using Deep Learning

emp

facial-recognition-via-cnn

This is a facial recognition method by using Convolutional Neural Network. In this project we can distinguish between images by extracting features of images

gradientboosting

Just like AdaBoost, Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. However, instead of tweaking the instance weights at every iteration like AdaBoost does, this method tries to fit the new predictor to the residual errors made by the previous predictor.

image-augmentation-master

Master all types of augmentation in image pre-procesing Useful in CNN research

kanban-board

lookuppipeline

This project is about using pipeline query in mongoose and nodejs

movies-recommender-systems

In this project we will focus on providing a basic recommendation system by suggesting items that are most similar to a particular item, in this case, movies.

next-amazona-2

This is for good practice

next-tailwind-amazona

Build Ecommerce Like Amazon By NEXT JS

object-detection-using-yolo

Used tensorflow, keras.  Used Darknet-53 to classify 80 classes  Used pre-trained weight for predictions in  Compared with RCNN, Fast RCNN  Pre-work done on SDD for object detection

prismats

This is full stack web application involving prisma, tailwind, typescript, postgreSQL and nextjs framework.

project_web3.0

This is a code repository for the corresponding video tutorial. In this video, we're going to build a Web 3.0 React Solidity Blockchain Application

react-curd

This repo contains code for curd in mern application

react-navbar

react-website

story

My little piece of work

story2

swagger-emp

implementing swagger ui in nodejs, mongoose backend app

talk

This is react chat app

test

testmern

this is mern test application with authentication

text-analysis

Here in Text Analysis I have used Word Cloud generator. Word clouds or tag clouds are graphical representations of word frequency that give greater prominence to words that appear more frequently in a source text. The larger the word in the visual the more common the word was in the document(s). This type of visualization can assist evaluators with exploratory textual analysis by identifying words that frequently appear in a set of interviews, documents, or other text.

video-server