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Karolis Koncevičius
Karolis Koncevičius
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We  ,sort sort of, do something like this effectively, especially in Gradient descent algorithms. A random line is simply a set of random parameters $\beta_0,\beta_1$. The gradient descent algorithm has to start somewhere looking for the optimal parameters, and the random set of parameters is one place to start.

So, in a way, we do start with a line, though we don’t draw it. Also, the algorithm itself is not exactly the one presented, of course. The instructor was probably trying to explain it without the notion of a gradient, and it’s tough. So, I’d give him a pass on a sloppy attempt.

We  ,sort of, do something like this effectively, especially in Gradient descent algorithms. A random line is simply a set of random parameters $\beta_0,\beta_1$. The gradient descent algorithm has to start somewhere looking for the optimal parameters, and the random set of parameters is one place to start.

So, in a way, we do start with a line, though we don’t draw it. Also, the algorithm itself is not exactly the one presented, of course. The instructor was probably trying to explain it without the notion of a gradient, and it’s tough. So, I’d give him a pass on a sloppy attempt.

We, sort of, do something like this effectively, especially in Gradient descent algorithms. A random line is simply a set of random parameters $\beta_0,\beta_1$. The gradient descent algorithm has to start somewhere looking for the optimal parameters, and the random set of parameters is one place to start.

So, in a way, we do start with a line, though we don’t draw it. Also, the algorithm itself is not exactly the one presented, of course. The instructor was probably trying to explain it without the notion of a gradient, and it’s tough. So, I’d give him a pass on a sloppy attempt.

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Aksakal
Aksakal
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We sort,sort of do, essentiallydo something like this effectively, especially in Gradient descent algorithms. A random line is simply a set of random parameters $\beta_0,\beta_1$. The gradient descent algorithm has to start somewhere looking for the optimal parameters, and the random set of parameters is one place to start. 

So, in a way, we do start with a line, though we don’t draw it. Also, the algorithm itself is not exactly the one presented, of course. The instructor was probably trying to explain it without the notion of a gradient, and it’s tough. So, I’d give him a pass on a sloppy attempt.

We sort of do, essentially, especially in Gradient descent algorithms. A random line is simply a set of random parameters $\beta_0,\beta_1$. The gradient descent algorithm has to start somewhere looking for the optimal parameters, and the random set of parameters is one place to start. So, in a way, we do start with a line, though we don’t draw it.

We ,sort of, do something like this effectively, especially in Gradient descent algorithms. A random line is simply a set of random parameters $\beta_0,\beta_1$. The gradient descent algorithm has to start somewhere looking for the optimal parameters, and the random set of parameters is one place to start. 

So, in a way, we do start with a line, though we don’t draw it. Also, the algorithm itself is not exactly the one presented, of course. The instructor was probably trying to explain it without the notion of a gradient, and it’s tough. So, I’d give him a pass on a sloppy attempt.

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Aksakal
Aksakal
  • 62.3k
  • 6
  • 106
  • 206

We sort of do, essentially, especially in Gradient descent algorithms. A random line is simply a set of random parameters $\beta_0,\beta_1$. The gradient descent algorithm has to start somewhere looking for the optimal parameters, and the random set of parameters is one place to start. So, in a way, we do start with a line, though we don’t draw it.