의료AI
번호 ⓒ 제 목 추천
교수 추천 주제 (계속 업데이트)
기말과제 관련 자료 (기사, 기술 전망, 저널논문, 석사/박사학위 논문, 오픈 소스 등)
참고 사이트
213       └❸ lThe idea behind Actor-Critics and how A2C and A3C improve them 1
212       └❸ lHow Positional Embeddings work in Self-Attention (code in Pytorch) 6
211       └❸ lWhy multi-head self attention works: math, intuitions and 10+1 hidden insights 3
210       └❸ lIntroduction to 3D medical imaging for machine learning: preprocessing and augmentations 2
209       └❸ lExplainable AI (XAI): A survey of recents methods, applications and frameworks 5
208       └❸ lIn-layer normalization techniques for training very deep neural networks 2
207       └❸ lBest Graph Neural Network architectures: GCN, GAT, MPNN and more 4
206       └❸ lHow Graph Neural Networks (GNN) work: introduction to graph convolutions from scratch 8
205       └❸ lGANs in computer vision - Improved training with Wasserstein distance, game theory control ... 1
204       └❸ lGANs in computer vision - Introduction to generative learning 2
203       └❸ lHow diffusion models work: the math from scratch 0
202       └❸ lTransformers in computer vision: ViT architectures, tips, tricks and improvements 5
201       └❸ lHow the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words 2
200       └❸ lHow Transformers work in deep learning and NLP: an intuitive introduction 7
199       └❸ lHow Attention works in Deep Learning: understanding the attention mechanism in sequence mod... 4
198       └❸ lThe theory behind Latent Variable Models: formulating a Variational Autoencoder 4
197       └❸ lHow to Generate Images using Autoencoders 0
196       └❸ lRecurrent neural networks: building a custom LSTM cell 2
195       └❸ lBest deep CNN architectures and their principles: from AlexNet to EfficientNet 5
194       └❸ lA journey into Optimization algorithms for Deep Neural Networks 6

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