Predicting & Classifying Brain tumor using CNN model

Classify the handwritten letters EMNIST

Academic: Deep learning-based mobile app to accurately identify the presence and severity of cataracts, enabling early diagnosis and treatment.

This is the official implementation of our research paper "Non-residential electricity day ahead load forecasting using a Transformer based adversarial domain adaptation forecaster"

MOT using deepsort and yolov3 with pytorch

An AI model that Classifies between 4 classes of Brain Tumors. Well-established CNN architecture pre-trained on a massive dataset of MRI scans. VGG16 model is used for this task.

Collection of the assignments for Data Science Engineering Methods on National Stock Exchange Dataset and TMNIST dataset

Pre-trained Deep Learning models and demos (high quality and extremely fast)

Pytorch to Tensorflow

A web app that classifies the type of bird

Constructed an advanced deep learning model utilizing TensorFlow and CNN to precisely classify bird species from audio inputs. Leveraging Librosa for audio processing, the model is trained on extensive bird sound datasets to ensure robust performance. Integrated with Hugging Face, our application facilitates seamless uploading of bird sound samples

Deep learning with tensorflow2.0, keras and python.

Demo showcasing Federated Learning with Flower for yoga pose classification, enabling collaborative training across distributed datasets while preserving data privacy.

Deep Learning models are used for audio emotion classification for six classes (happy, sad, angry, disgusted, fearful, and neutral) and uses parallel computing to speed up preprocessing and feature extraction.

Detection of Human Edited Images using CNN, VGG16, Xception, ELA, Ensemble Learning.

Multilayer Authenticity Identifier (MAI), a CNN model that attempts to identify synthetic AI images.

Exploring ComputerVision from scratch

This project aims to detect pneumonia from chest X-ray images using a Convolutional Neural Network (CNN). The model is trained on a dataset of chest X-ray images and evaluated for its performance. The project is ongoing, and I aim to fine-tune the model in the future. If you are seeing this, it means I am still working on the project.

In this repository, I implemented the deep learning classifier introduced in the paper "Deep Learning to Improve Breast Cancer Detection on Screening Mammography" using PyTorch.