[학습] 모두를 위한 딥러닝 #1 (Lec 00 ~ Lec 04)

강의 웹사이트: http://hunkim.github.io/ml/

Facebook: https://www.facebook.com/groups/Tenso...

소스코드: https://github.com/hunkim/DeepLearnin...

 

 

Lec 00 - Machine/Deep learning 수업의 개요와 일정

• Andrew Ng’s and other ML tutorials
  • https://class.coursera.org/ml-003/lecture
  • http://www.holehouse.org/mlclass/ (note)
  • Deep Learning Tutorial
• Convolutional Neural Networks for Visual Recognition.
  • http://cs231n.github.io/
• Tensorflow
  • https://www.tensorflow.org
  • https://github.com/aymericdamien/TensorFlow-Examples
  • Deep learning @Udicity

 

Lec 01 - 머신러닝의 개념과 용어

 

1. Machine Learning

  - "Field of study that gives computers the ability to learn without being explicitly programmed” Arthur Samuel (1959)

 

2. Supervised/Unsupervised learning

  - Supervised learning:

     . learning with labeled examples

  - Unsupervised learning: un-labeled data

     . Google news grouping

     . Word clustering

 

3. Types of supervised learning

  • Predicting final exam score based on time spent

    - regression

  • Pass/non-pass based on time spent

    - binary classification

  • Letter grade (A, B, C, E and F) based on time spent

    - multi-label classification

Lec 02 - Linear Regression의 Hypothesis 와 cost 설명

1. Linear hypothesis

    H(x) = W x + b

 

2. Cost

    H(x) - y

  

 

     H(x) = W x + b

 

   

 

  3. Goal : Minizie Cost

 

      minimize cost(W,b)

 

 

 

Lec 03 - Linear Regression의 cost 최소화 알고리즘 설명

 

1. Hypothesis and Cost

• H(x) = W x

        

 

 2. Gradient descent algorithm Minimize cost function

• formal definition

        

• How about Newton-Rapshon algorithm ?

 

 3. Derivative Calculator : [link]

 

 4. Check cost function 

• must be convex curve

 

Lec 04 - Multi-variable linear regression

 

1. Hypothesis for multi-variable

 

   

 

2. Hypothesis using Matrix