[Coursera]Neural network and deep Learning: Week2

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
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작성자: 김태완
1999년 부터 Java, Framework, Middleware, SOA, DB Replication, Cache, CEP, NoSQL, Big Data, Cloud를 키워드로 살아왔습니다. 현재는 빅데이터와 Machine Learning을 중점에 두고 있습니다.
E-mail: taewanme@gmail.com