Let’s continue!

Today is mainly learning about “Decision boundary”, “Cost function of logistic regresion”, “Logistic loss” and “Gradient Descent Implementation for logistic regression”.

We found out the “Decision boundary” is when z equals to 0 in the sigmod function.

Because at this moment, its value will be just at neutral position.

Andrew gave an example with two variables, x₁ + x₂ – 3 (w₁ = w₂ = 1) the decision bounday is the line of x₁ + x₂ = 3.

I want to say “Cost function for logistic regression” is the most hard in week 3 so far I’ve seen.

I haven’t quite figured out why the square error cost function not applicable and where the loss function came from.

I have to re-watch the videos again.

The lab is also useful.

This particular cost function in above is derived from statistics using a statistical principle called maximum likelihood estimation (MLE).

Questions and Answers

1. Why do we need loss function?
   Logistic regression requires a cost function more suitable to its non-linear nature. This starts with a Loss function.
2. Why is the square error cost function not applicable to logistic regression?
3. What is maximum likelihood?
4. In logistic regression, “cost” and “loss” have distinct meanings. Which one applies to a single training example?
   A: The term “loss” typically refers to the measure applied to a single training example, while “cost” refers to the average of the loss across the entire dataset or a batch of training examples.

Some thoughts of today

Honestly, it feels like it’s getting tougher and tougher.

I can still get through the equations and derivations alright, it’s just that as I age, I feel like my brain is just not keeping up.

At the end of each video, Andrew always congratulates me with a big smile, saying I’ve mastered the content of the session.

But deep down, I really think what he’s actually thinking is, “Ha, got you stumped again!”

However, to be fair, Andrew really does explain things superbly well.

I hope someday I can truly master this knowledge and use it effortlessly.

Fighting!