⌨ () Introduction
⌨️ () Introduction
I think you could have used an « else » here :)Great video !
⌨ () Colab intro (importing wine dataset)
⌨️ () Colab intro (importing wine dataset)
X = df[df.columns[]].valuesy = df[df.columns[-1]].values
[] Just to be clear, `NaN` is not a "none-type value" indicating that "no value [was] recorded [there]" —that'd be `undefined`. It stands for "not a number" and is the result returned from trying to do an operation that can only be done on an Int/Float (or something that will be coerced into an Int/Float) on a value that isn't an Int/Float; e.g., `4 * "dog"` in JS will return `NaN`. It means you tried to do something with a number that's irrational to do with an number. Another JS example: zero divided by zero.
⌨ () What is machine learning?
⌨️ () What is machine learning?
⌨ () Features (inputs)
⌨️ () Features (inputs)
🥺💬 That is almost Thailand I will continue to watch this VDO but I fill feed all cats and the velocity time.
Oh man, was fasting today and the example at around with the hot dog, pizza, and ice cream had me dying😅
⌨ () Outputs (predictions)
⌨️ () Outputs (predictions)
@ when kylie was explaining multiclass and binary classification with the example of hotdog, I first remembered Jian yang's app from Silicon Valley. I really liked that you put in a small clip of it.
Most funny moment 😂
⌨ () Anatomy of a dataset
⌨️ () Anatomy of a dataset
⌨ () Assessing performance
⌨️ () Assessing performance
⌨ () Neural nets
⌨️ () Neural nets
⌨ () Tensorflow
⌨️ () Tensorflow
⌨ () Colab (feedforward network using diabetes dataset)
⌨️ () Colab (feedforward network using diabetes dataset)
⌨ () Recurrent neural networks
⌨️ () Recurrent neural networks
⌨ () Colab (text classification networks using wine dataset
⌨️ () Colab (text classification networks using wine dataset)--
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