Linear classifiers (1): Basics

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  • เผยแพร่เมื่อ 8 ต.ค. 2024

ความคิดเห็น • 13

  • @prraoable
    @prraoable 6 ปีที่แล้ว +3

    Thanks a ton for posting these!!! I am learning SO much more from these videos than I am in my actual class! Why can't more professors teach through high quality recorded lectures rather than write shoddy equations on a whiteboard?

  • @angieh26o
    @angieh26o 7 ปีที่แล้ว +2

    Seriously, these are some incredibly clear lectures - so good!

  • @satellitesabunim
    @satellitesabunim 10 ปีที่แล้ว +12

    Your lecture is very wonderful. It seems these videoes are series. But it is hard to find right order. If you put the number in front of video such as 1.1, 1.2, .... 2.1... and so on, it would be very helpful to follow all your lecture.
    Thanks.

    • @tejasudha
      @tejasudha 6 ปีที่แล้ว +1

      sli.ics.uci.edu/Classes/2015W-273a

  • @jzbgtiger
    @jzbgtiger 6 ปีที่แล้ว

    One of the best classes on Internet

  • @kehsoon
    @kehsoon 5 ปีที่แล้ว

    Very well explained on linear regression. Visually intuitive. Thank you.

  • @theSpicyHam
    @theSpicyHam ปีที่แล้ว

    excellent presentation

  • @mustardman4966
    @mustardman4966 3 ปีที่แล้ว

    In contrast to real numbers that have the property of varying "smoothly", the objects studied in discrete mathematics - such as integers, graphs, and statements in logic - do not vary smoothly in this way, but have distinct, separated values. ... The set of objects studied in discrete mathematics can be finite or infinite.

  • @xinxu1832
    @xinxu1832 7 ปีที่แล้ว

    FANTASTIC

  • @tausal1
    @tausal1 6 ปีที่แล้ว

    Excellent!

  • @ccerrato147
    @ccerrato147 8 ปีที่แล้ว

    Nice lesson!

  • @mustardman4966
    @mustardman4966 3 ปีที่แล้ว

    Modeling a neuron 2:10

  • @natemerrill7637
    @natemerrill7637 6 ปีที่แล้ว

    A perceptron needs the nonlinear activation! Your definition is not right!!