This project implements a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset, which consists of 60,000 32x32 color images across 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.

Skin cancer can be broadly classified into two major categories: Melanoma (Malignant) and non-melanoma (Benign). Melanoma is one of the deadliest kinds of cancer. However, the detection of this cancer at an early stage can help in improving the chances of survival.

Exoplanet Hunting in Deep Space.

Pistachios are nutritious nuts that are sorted based on the shape of their shell into two categories: Open mouth and Closed mouth. The open-mouth pistachios are higher in price, value, and demand than the closed-mouth pistachios. Because of these differences, it is considerable for production companies to precisely count the number of each kind.

batch_normalization through PyTorch

PyTorch module FLOPS counter

Model Optimization using Batch Normalization and Dropout Techniques

Cats vs dogs classification using deep learning. Data augmentation and convolutional neural networks.

This GitHub repository aims to provide comprehensive overview of the evolution of GoogLeNet, a popular Convolutional Neural Network architecture developed by Researchers at Google for image classification tasks.

This project aims to develop an advanced DL model using CNN to accurately detect and classify brain tumors from MRI scans.

prediction of an absolute temperature on the surface of a star using neural networks

Developed CNN model with 93% validation accuracy using techniques like Dropouts & Batch Normalization. Using haar cascade of Computer Vision It is then used to detect sunglasses on real time basis

Keras Convolutianl Networks

This repository contains my project for computer vision.

Deep learning Simple models

Batch normalization from scratch on LeNet using tensorflow.keras on mnist dataset. The goal is to learn and characterize batch normalization's impact on the NN performance.

Fundamentals of Artificial Intelligence and Deep Learning Frameworks

Developing deep learning models with only numpy and panda and without using high level libraries such as Tensorflow, Keras and PyTorch

Charts and Fake image (OCR on Charts)