<aside> 💡

본 페이지는 Simon J.D. Prince 선생님의 “Understanding DeepLearning” 책을 번역하여 공부한 내용입니다.

https://udlbook.github.io/udlbook/

</aside>

실습

🛠️Transformer Implementation

🛠️[데이콘] 도배 하자 분류

이론

📚[1-2] Introduction & Supervised Learning

📚[3] Shallow Neural Networks

📚[4] Deep Neural Network

📚[5] Loss functions

📚[6] Fitting Models

📚[7] Gradients and Initialization

📚[8] Measuring Performance

📚[9] Regularization

📚[10] Convolution Networks

📚[11] Residual Networks

📚[12] Transformers

📚[13] Graph neural networks

📚[15] Generative Adversarial Networks

📚[16] Normalizing Flow