Models supported: ResNet, ResNetV2, SE-ResNet, ResNeXt, SE-ResNeXt [layers: 18, 34, 50, 101, 152] (1D and 2D versions with DEMO for Classification and Regression).

Models Supported: Inception [v1, v2, v3, v4], SE-Inception, Inception_ResNet [v1, v2], SE-Inception_ResNet (1D and 2D version with DEMO for Classification and Regression)

2D image convolution example in Python

Models Supported: VGG11, VGG13, VGG16, VGG16_v2, VGG19 (1D and 2D versions with DEMO for Classification and Regression).

GPU (CUDA) accelerated filters using 2D convolution for high resolution images.

A General Medical Image Segmentation Framework.(Multi-Modal, Mono-Modal, 2D, 3D)

Digital Image Processing with Matlab

2D Convolution apply in Frequency Domain

A collection of Jupyter notebooks containing various MNIST digit and fashion item classification implementations using fully-connected and convolutional neural networks (CNNs) built with TensorFlow and Keras. 2020.

Here is the Computer assignments material I designed for the Signals & Systems Winter 1400, instructed by Dr. Rabiei.

Noise2Noise is an AI denoiser trained with noisy images only. We implemented a ligther version which trains faster on smaller pictures without losing performance and an even simpler one where every low-level component was implemented from scratch, including a reimplementation of autograd.

Two-Dimension (2D) Convolution Processor Design

Set of 2D & 1D CNN models to classify images of handwritten numbers from the MNIST dataset using Keras.

This repository contains Convolutional Neural Networks implemented from scratch.

NLP Project - Sentence Classification - Toxicity- Approx 20,000 comments - ranging from 2 to 30 words. Balanced Data Set. 1. Traditional, pre-2010 NLP and ML techniques used. 2. Dense Word Vectors - w2v & Glove, sentence vector created from averaged word vectors, ANN. 3. Glove combined with bi-LSTMs and 2D Convs.

Speech Emotion Recognition using 1D and 2D Convolutional Neural Networks

A larger data science project that uses a 2D Convolutional Neural Network to determine the likelyhood of having covid-19 via CT images

University project of the Parallel Computing course. There are two challenges that are solved using parallel computing techniques and two famous frameworks: OpenMP and CUDA.