Defect detection on metal shaft surfaces using Convolutional Neural Network
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Updated
Jun 1, 2024 - Python
Defect detection on metal shaft surfaces using Convolutional Neural Network
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
A series of machine learning trigger bots for Counter-Strike: Global Offensive (CS:GO).
Common machine learning algorithm implementations
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.
A classical or convolutional neural network model with adversarial defense protection
Convolutional neural network capable of identifying skin lesions (based on the skin lesion image data set HAM10000).
Rectangle detection and stress simulation tool developed for the UVA I2SEE Civil Engineering Lab
A machine learning trigger bot for Quake3 Arena & Quake Live.
Implementation of Convolutional Neural Network or ConvNet from scratch.
Code for the "Learning to Estimate Two Dense Depths from LiDAR and Event Data" article
Developed and evaluated machine learning and deep learning models for detecting financial fraud.
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