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.
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Updated
Jul 15, 2024 - Pascal
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.
The project presents a Deep Learning model with an autoencoder-like architecture making use of convolutional layers in both the encoder and the decoder to perform image inpainting over the CIFAR-10 database images reaching a mean mean-squared error value of 0.007867775.
This project demonstrates image classification using a Convolutional Neural Network (CNN) on the CIFAR-10 dataset. The model is trained to classify images into one of 10 classes.
Develop an AI-based image classification system using CNN and transfer learning. The project includes data preprocessing, model training, fine-tuning, evaluation with precision, recall, and F1-score, and testing.
A series of experiments on the CIFAR10 Dataset using Convolutional Neural Networks.
CNN training strategy with blurred images
Submission for Intro to Machine Learning with Pytorch Deep Learning Project
A summative coursework for CSC8635 Machine Learning with Project
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.
My fundamental topics - research on Adversarial machine learning
PyTorch implementation of a 9-layer ResNet for CIFAR-10.
sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data
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.
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