TensorFlow (Keras) implementation of MobileNetV3 and its segmentation head

Spatial and Channel Attention in CNN Architectures for Image Classification task

CNN Architectures(with and without SPP Layer) for Image Classification task

Repo for the paper "Deep Multimodal Transfer-Learned Regression in Data-Poor Domains"

Malaria-Detection-Using-CNN

Application of ARMA Networks on Image Segmentation task

The code for Task 3 of the ISIC2018 Deep Learning challenge with the following goal: “Submit automated predictions of disease classification within dermoscopic images” and therefore is a great testbed to measure the performance of different CNN architectures."

Keras implementation of influential CNN architectures.

Implementation of Dilated VGG Networks for Image Classification task

This repository contains coding files that will help us determine which CNN model architecture performs best at classifying a dog and its various breeds. This project was completed as part of AI Programming with Python Nanodegree program (Udacity).

This was just a project regarding the basics of deep learning. And it has covered most of the basics needed to get an idea regarding hyperparameter tuning, different CNN architecture etc.

Kaggle project keymap to GitHub based on Data Science.

Implementation of Dilated Residual Networks for Image Classification task

This folder contains my reading notes on different topics in Machine Learning.

Driver's Drowsiness Prediction Using CNN Architechtures

Advanced concepts in computer vision, covering topics such as image classification, object detection, and transfer learning techniques for enhanced model performance and accuracy.

[Unmaintained] Notes and implementations of some papers that I had read 📄

Different CNN Architectures for Medical Image Segmentation task

Complete introduction to deep learning with various architechtures. Maths involved is also included. Code samples for building architechtures is included using keras. This repo also includes implementation of Logical functions AND, OR, XOR.