High-efficiency floating-point neural network inference operators for mobile, server, and Web
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Updated
May 23, 2024 - C
High-efficiency floating-point neural network inference operators for mobile, server, and Web
Программы по дисциплине "Современные методы глубокого машинного обучения" 6 семестра ФИТ НГУ
Framework for the reproducible processing of neuroimaging data with deep learning methods
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.
Common machine learning algorithm implementations
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.
Defect detection on metal shaft surfaces using Convolutional Neural Network
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
Developed a gesture recognition system for Indian Sign Language (ISL) using Convolutional Neural Networks. Designed to bridge communication gaps between the hearing impaired and normal individuals, facilitating effective interaction.
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