adaptive-denoising

Created for the trained and pre-processed dataset to clear the noise and grains from image before detection

apple

back-end-of-medimind

all the backend foile

coremltools

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

devx

face-detection-javascript

genx-portfolio

I have creatd a portfolio website using NextJS, Framer-Motion, Tailwind CSS, and Javascript

hello-world

I HAVE MADE A ALGORITHM

imac-portal

This is a repository of booking iMac in SRM lab

image-engine-model

This is a model ENGINE develop using ML concept to enhance images with seven separate layers of image model

image-processing-using-gan-model

GAN model is a generative adversial network which is used for the development and upscaling the images to increase it's sharpness and details

leetcode-questions-companywise

Contains Company Wise Questions sorted based on Frequency and all time

ml-ferret

ml-stable-diffusion

Stable Diffusion with Core ML on Apple Silicon

my-portfolio

nlp-for-twitter-sentiment-analysis-of-vernacular-languages

This repo deals with the new way of sentiment analysis that provide the analysis of vernacular languages.

pixtagram

plant-species-identification

python-uri-to-url

Converting URI to URL

python_url

raspberry-pi-hi-quality-camera-code

This repository contain the RASPBERRY PI Hi-Quality Camera functional code. Using Python module I hav e created a code that can be easlily installed in the RASPBERRY PI 3 or any upcoming RASPBERRY PI. This file contain the capturing code of 12 bit RAW image from RASPBERRY PI SENSOR

real-time-auto-white-balance

The real-time auto white balance is coded in such a way that can be run in any kind of microprocessor.

real-timeimage-capturing-using-

skin-disease-ai

this is the model developed for the preliminary diagnosis of any kind of skin disease

story-2

suraj209211

Config files for my GitHub profile.

tone_recognition

This project is related to the CNN and RNN. Here CNN receives images of fixed size and outputs a predicted class label for each image along with a confidence level. In RNNs, the size of the input and the resulting output can vary.