Coursera에서 deeplearning.ai가 운영하는 ‘Neural network and deep learning[↗NW] ]‘의 2주차 강의 정리입니다.

• 강의 구성
  • Logistic Regression as a Neural Network
    • Binary Classification
    • Logistic Regression
    • Logistic Regression Cost Function
    • Gradient Descent
    • Derivatives
    • More Derivative Examples
    • Computation Graph
    • Derivative with a computation graph
    • Logistic Regression Gradient Descent
    • Gradient Descent m Examples
  • Python and Vectorization
    • Vectorization
    • More Vectorization Examples
    • Vectorizing Logistic Regression
    • Vectorizing Logistic Regression’s Gradient Output
    • Broadcasting in Python
    • A note on python/numpy vectors
    • Quick tour of jupyter notebooks
    • Explanation of logistic regression cost function

Logistic Regression as a Neural Network

• for문을 지향하고 Vectorization을 수행
• forward propagation과 back propagation을 소개

Binary Classification

• Logistic regression은