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.

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

Implementation of key concepts of neuralnetwork via numpy

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

Implemented GPT from scratch

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

Decision Tree Implementation from Scratch

C codes for the Arificial Intelligence Course and algorithms.

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

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

Handwritten digit classification system with custom neural networks from scratch. 96.53% accuracy on MNIST with interactive GUI for real-time testing.

Breast Cancer Classification with Logistic Regression

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.

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

Comparison of common loss functions in PyTorch using MNIST dataset

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.

Neural Networks from scratch (Inspired by Michael Nielsen book: Neural Nets and Deep Learning)

Implementation of a Fully Connected Neural Network, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) from Scratch, using NumPy.

This repository contains two models having Two - layers ANN and L - layers ANN respectively to classify Cat photo and Non-Cat photo. This ANN works on the mathematical principles of Logistic Regression and Cross Entropy.

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full visualization of netflix and movielense datasets with 89% accuraccy item2vec

Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".

IMAGE CLASSIFICATION USING CNN and MobileNetV3

Digital Image Processing Course | Home Works Design| Fall 2021 | Dr. MohammadReza Mohammadi