Binary and Categorical Focal loss implementation in Keras.

Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"

A PyTorch implementation of U-Net for aerial imagery semantic segmentation.

Implementation of key concepts of neuralnetwork via numpy

Code for the AAAI 2022 publication "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

Pytorch Implementations of Common modules, blocks and losses for CNNs specifically for segmentation models

The most basic LSTM tagger model in pytorch; explain relationship between nll loss, cross entropy loss and softmax function.

A Feed Forward Neural Network which a ReLU activation, Cross Entropy Loss & Adam Optimizer

Decision Tree Implementation from Scratch

C codes for the Arificial Intelligence Course and algorithms.

Code for the Paper : NBC-Softmax : Darkweb Author fingerprinting and migration tracking (https://arxiv.org/abs/2212.08184)

Practice using PyTorch include data preprocessing, linear algebra, optimization, neural networks, CNNs, and more to cover ML and DL basics

Neural Network to predict which wearable is shown from the Fashion MNIST dataset using a single hidden layer

A classifier to differentiate between Cat and Non-Cat Images

Breast Cancer Classification with Logistic Regression

Comparison of common loss functions in PyTorch using MNIST dataset

Multiclass Classification using Softmax from scratch without any famous library like Tensorflow, Pytorch, etc.

This repository contains code for the PhD thesis: "A Study of Self-training Variants for Semi-supervised Image Classification" and publications.

Maths behind machine learning and some implementations from scratch.

Neural network-based character recognition using MATLAB. The algorithm does not rely on external ML modules, and is rigorously defined from scratch. A report is included which explains the theory, algorithm performance comparisons, and hyperparameter optimization.