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artificial-intelligence

SEU CS Artificial Intelligence Projects

awesome-transformers-in-medical-imaging

A collection of resources on applications of Transformers in Medical Imaging.

book

个人认为对技术提升很不错的书

books

技术书籍等

brain-tokyo-workshop

🧠🗼

char-rnn-tensorflow

Multi-language Char RNN for TensorFlow >= 1.2.

course_work

course work of undergraduate studies

ct-wgan_vgg_tensorflow

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

ctformer

This repository includes implementation of CTformer on Low-dose CT Denoising

deep-learning-coursera

Deep Learning Specialization by Andrew Ng, deeplearning.ai.

deep-learning-keras-tensorflow

Introduction to Deep Neural Networks with Keras and Tensorflow

densenet-tensorflow

Simple Tensorflow implementation of Densenet using Cifar10, MNIST

densenet-tensorflow-1

Reimplementation of DenseNet

doing_the_phd

ds-take-home

My solution to the book A Collection of Data Science Take-Home Challenges

eece-6540-labs

labs and exercises for EECE.6540 Heterogeneous Computing at UMass Lowell

evolutionary-algorithm

Evolutionary Algorithm using Python

factorizer

Pytorch implementation of Factorizer.

gpt-3

GPT-3: Language Models are Few-Shot Learners

hello

heterogeneous_computing

learning-in-the-frequency-domain

low-dose-ct-denoising

Code and papers for Low-Dose CT denoising

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

manifoldron

This respository includes implementations on Manifoldron: Direct Space Partition via Manifold Discovery

matchingnetworks

An attempt at replicating the Matching Networks for One Shot Learning in Tensorflow - Paper URL: https://arxiv.org/pdf/1606.04080.pdf

nowcoder

pytorch-dsfnet

DSFNet: Convolutional Encoder-Decoder Architecture Combined Dual-GCN and Stand-alone Self-attention by Fast Normalized Fusion for Polyps Segmentation

qae

Quadratic Autoencoder based on new types of neurons and its application in low-dose CT

relinear

An effective algorithm for training a quadratic network