moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.

Image enhancement using CNN and LVM

High-efficiency floating-point neural network inference operators for mobile, server, and Web

This project explores the effectiveness of FFT filters and DnCNN denoising in improving image quality by reducing noise in digital images.

EBOP Model Automatic input Value Estimation Neural network

The project aims to reduce noise from audio files using signal processing and deep learning techniques.

Detection and Classification of tomato diseases for farmers in Nigeria using cutting-edge convolution neural networks (CNN) and leveraging the power of VGG19 model as a transfer model.

A pipeline for semantic segmentation, densification, and planar flattening for improving voxelization and mesh reconstruction quality of airborne LiDAR data.

This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based on accelerate/PyTorch, as well as scripts for experiments with CIFAR-10 and Tiny ImageNet.

An AI cat breed image classifier built using TensorFlow and Keras

Project developed for the Numeric Simulation Laboratory A.A. 2023-2024, held by professor Davide Emilio Galli at the University of Milan, Physics Department.

A Speech Recognition Framework for Banking Interactions using Convolutional Recurrent Dense Neural Networks and Language Models

An open-source deep learning framework for data mining of protein-protein interfaces or single-residue variants.

Open source Python library for building bioimage analysis pipelines

This toolkit is a curated collection of machine learning projects, resources, and utilities designed to assist both beginners and seasoned practitioners in their journey through the fascinating world of machine learning.

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

A solid foundational understanding of XAI, primarily emphasizing how XAI methodologies can expose latent biases in datasets and reveal valuable insights.

Tutorials on machine learning, artificial intelligence, data science with math explanation and reusable code (in python and R)

This project aims to improve early lung cancer detection using deep learning. It utilizes a pretrained EfficientNet model to classify histopathological lung images, and a Gradio interface for real-time predictions.

A javascript ML library. Make complicated or simple AI deep learning models and train them on anything. STAR THE PROJECT == I GET MOTIVATED TO MAKE IT BETTER