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Non-linear Hypotheses

今天是傳說中的 7/8 日!! 哈哈哈

  • Motivation- Non-linear Classification:
    • 如果今天 features 有一堆!
    • ex: 房子/房價
        x1 = size,        # quadratic features (o(n^2)) about: (n^2)/2  [n --> features]
        x2 = # bedrooms   ====> ≈ 5000 features
        x3 = # floors   
        x4 = age          
        .                 # cubic features (o(n^3))
        .                 ====> 170,000 features
        .                 
        x100
      
    • for many machine learning problems: n will be pretty large
  • Example n will be pretty large:
    • Compouter Vision: Car detection
    • EX:
       50 x 50 pixel images -> 2500 pixels
       n = 2500 (7500 if RGB)
             
                |   pixel 1 intensity   | ---> (0 ~ 255)
                |   pixel 2 intensity   |
           x =  |         .             |
                |         .             |
                |  pixel 2500 intensity |
                      
       # Quadratic features (xi * xj): ≈ 3 million features
      

當 features 超級多的時候,用 logistic regression 不是一棒棒的方法去學 complext nonlinear hypothese.

Neurons and the Brain

Neural Networks

  • Origins: Algorithms that try to mimic the brain.
  • Was very widely used in 80s and early 90s; popularity diminised in late 90s.
  • Recent resurgence: State-of-the-art technique for many applications

不論送入甚麼訊號給大腦,大腦就是有能力可以去學習處裡它!!!