CAI NEURAL API - Pascal based deep learning neural network API optimized for AVX, AVX2 and AVX512 instruction sets plus OpenCL capable devices including AMD, Intel and NVIDIA.

This project demonstrates image classification on the CIFAR-10 dataset using transfer learning with the pre-trained VGG16 model. The implementation is done in Google Colab and includes data preprocessing, model adaptation, training, evaluation, and result visualization using TensorFlow and Keras.

An Image Classification project w/ MobileNetV2 and DenseNet-121. Leveraging techniques like Hyperparameter Tuning, Transfer Learning, Imagine Preprocessing Techniques and Ensemble Methods.

CIFAR-10 trained with CreateML and a SwiftUI app for real time model testing. Kaggle Score: 0.69660

Convert CIFAR-10 and CIFAR-100 datasets into PNG images

Scripts for downloading, preprocessing, and numpy-ifying popular machine learning datasets

ResNet model of high accuracy (on Cifar-10) with less than 5 million parameters.

Some mini-projects using well known datasets to practice important deep learning concepts.

Multiple machine learning algorithms to solve associated problems coupled with varying theoretical examinations.

A Pytorch implementation of ResNet trained on cifar-10 with accuracy of 92.07%

PyTorch Implementation of AlexNet architecture on the CIFAR 10 dataset.

This repository hosts the programming exercises for the course "Machine Learning" of AUEB Informatics.

In this project we will train stable diffusion model on CIFAR10 dataset and then try to generate images form ten different classes.

Official code for "PubDef: Defending Against Transfer Attacks From Public Models" (ICLR 2024)

Training a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 database. The CIFAR-10 dataset consists of small color images grouped into ten classes, including objects like airplanes, automobiles, birds, cats, and more.

A simple Convolutional Neural Network trained on the famous CIFAR-10 dataset. Implemented in PyTorch.

classifying CIFAR-10 images using CNN in Tensorflow and Keras

ResNet with Shift, Depthwise, or Convolutional Operations for CIFAR-100, CIFAR-10 on PyTorch

The code does image classification using the CIFAR-10 dataset. Two models, ANN and CNN, are trained on 32x32 color images across 10 classes. Following data preprocessing, the models are constructed and trained. Their classification performance is assessed on test images, highlighting their effectiveness in identifying objects within the dataset.