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big-basket-chatbot

brain-tumor-detection-with-cnn-based-model

chicken-disease-classification

cifar-10-classification-by-alexnet-in-pytorch

This code contains the implementation on AlexNet in PyTorch from scratch. It has been trained to classified CIFAR-10 dataset.

coursera-deep-learning-specialization

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks;

disaster-and-tweets

dl-based-collaborative-filtering-model-with-content-based-support

A book recommendation system that harnesses the power of Deeplearning based Collaborative Filtering complemented by content-based filtering to tackle the cold-start problem. Additionally, this model has the capability to recommend books based on external text queries, enhancing the versatility of the recommendations.

end-to-end-cnn-and-hybrid-cnn-rf-brain-tumor-detection

This project employs TensorFlow to develop a CNN-based brain tumor detection system. Moreover, a hybrid model, combining a pre-trained CNN as a feature extractor with a LightGBM Classifier, achieved even better performance, underscoring the efficacy of hybrid approaches in medical image analysis.

inception-based-fashion-mnist-classification-in-pytorch

This project showcases the creation of a custom inception block using PyTorch and its application to classify the MNIST Fashion dataset. The integration of F1 score and a confusion matrix enhances the evaluation process.

lifestyle-prediction-using-ensemble-learning

Designed an ensemble of diverse classifiers delivering an F1 score of 87.63% in predicting healthy vs. unhealthy lifestyles from tabular data

neural_style_transfer

noisy-and-low-light-image-enhancement-with-cnn

The goal of the project is to develop a computer vision system that can enhance low light and noisy images. Specifically, given an input image containing a region of interest (ROI) that is dark and/or noisy, the system should be able to produce an enhanced image of that ROI that is visually pleasing and improves the overall image quality.

py

Repository to store sample python programs for python learning

segmenter

[ICCV2021] Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation

sleep-stage-classification-from-eeg-ml-vs-dl

Implemented machine learning models (LGBM, CNN-LSTM) for sleep stage prediction from raw EEG signals, contributing to efficient diagnosis of sleep disorders.

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