Programming for AI Study Group

• Author: Jong Hyun Kim
• KAIST 김재철AI 대학원 Edward Yoonjae choi 교수님의 강의
• Lecture: https://www.youtube.com/watch?v=QKout3TEgWU&list=PLLENHvsRRLjDHllrXj0B8sz5-4xVbisBL&index=14
• Kaist - Grad School of AI , lecture by Edward Choi

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Study Plan

• Practice 1: Numpy (o)
• Class2: Basic Machine Learning (o)
• Practice 2: Basic Machine Learning (o)
• Practice 3: PyTorch & Neural Networks (o)
• Class 4: Autoencoder (o)
• Practice 4: Autoencoder (o)
• Class 5: Variational Autoencoder
• Practice 5: Variational Autoencoder
• Class 6: Generative Adversarial Network
• Practice 6: Generative Adversarial Network
• Class 7: Convolutional Neural Network
• Practice 7: Convolutional Neural Network
• Class 11: Transformer
• Practice 11: Transformer

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Objective

• 각자 공부한 code 매일 업로드 후 확인
• 수업시간에 주어진 homework
• build up한 모델 주어진 데이터 외에 다른 데이터를 가지고 적용해보기 (최소 3개의 새로운 데이터를 적용한 나만의 model build)
• 점심시간 이용해 서로 피드백 및 질문 사항 탐구

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