High-efficiency floating-point neural network inference operators for mobile, server, and Web
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Updated
Jun 12, 2024 - C
High-efficiency floating-point neural network inference operators for mobile, server, and Web
Framework for the reproducible processing of neuroimaging data with deep learning methods
This is the hub for all the projects I have worked on.
A simple sequential CNN architecture to classify three major types of hair (curly, wavy, straight).
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
Deep Leaning: Using keras library to classify cat and dog images
Repositório destinado aos projetos realizados na disciplina de Inteligência Computacional Aplicada (ICA) [2022.2] do PPGEEC.
Defect detection on metal shaft surfaces using Convolutional Neural Network
This repo contains my work & The code base for this TensorFlow Developer specialization offered by deeplearning.AI
Statistical Learning Project
Detecting diseases in maize plant leaves using convolutional neural network
A series of machine learning trigger bots for Counter-Strike: Global Offensive (CS:GO).
A system for recognizing and interpreting hand gestures using machine learning and computer vision techniques.
Программы по дисциплине "Современные методы глубокого машинного обучения" 6 семестра ФИТ НГУ
There are plenty of ways to approach supervised learning: Some of them being Neural Networks, Convolutional Neural Networks and Residual Networks. In this repository we develop an in depth analysis of the difference between these on the CIFAR10 dataset using Jupyter Notebooks and Pytorch.
Deep Learning in python
Developed a deep learning model using TensorFlow and CNN to accurately identify diseases in potato plants, optimizing crop health and yield. The model distinguishes between diseases such as early blight, late blight, and healthy plants from images with precision.
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