전자공학과 4학년 2학기 자동차 인공지능 강의

• Tensorflow 기초
  • Loss Function, Optimizer
• 회귀분석
  • 선형회귀, 비선형회귀
• 인공지능 기초
  • 단층 퍼셉트론
    • AND, OR, XOR 논리 학습
  • 다층 신경망
  • 가중치, 모델 저장
  • 모델 경량화 및 배포
• 심층 신경망
  • MNIST Dataset 실습
  • Normalization Algorithm
• CNN
  • Convolution
  • MNIST Dataset 실습
  • Fashion MNIST Dataset 실습
• RNN
  • RNN 기초
  • MNIST 실습
  • CIFAR10 실습
  • LSTM, GRU, Bidirectional RNN 실습
• GAN
  • MNIST 실습
  • Upgraded GAN 실습
  • Style Transfer 실습

Automotive artificial intelligence lectures in the second semester of the fourth year of electronic engineering

• Tensorflow Foundation
  • Loss Function, Optimizer
• Regression analysis
  • Linear regression, nonlinear regression
• Artificial Intelligence Fundamentals
  • single-layer perceptron
    • AND, OR, XOR Logic Learning
  • multilayer neural network
  • weights, saving models
  • Lighten and deploy models
• a deep neural network
  • MNIST Dataset Practice
  • Normalization Algorithm
• CNN
  • Convolution
  • MNIST Dataset Practice
  • Fashion MNIST Dataset Practice
• RNN
  • RNN Foundation
  • MNIST Practice
  • CIFAR10 Practice
  • LSTM, GRU, Bidirectional RNN Practice
• GAN
  • MNIST Practice
  • Upgraded GAN Practice
  • Style Transfer Practice