Linear Regression

After training for some predetermined number of iterations (or until some other stopping criteria are met), we record the estimated model parameters, denoted 𝐰̂ ,𝑏̂ w^,b^. Note that even if our function is truly linear and noiseless, these parameters will not be the exact minimizers of the loss because, although the algorithm converges slowly towards the minimizers it cannot achieve it exactly in a finite number of steps.

I have a question about the part in bold. If we choose a large learning rate, then the algorithm can overshoot the parameter values for which loss function is minimized. So, that tells me that we should be able to find w, b that minimize the loss exactly. What could I be missing?

For Q1 from the exercises, the solution for b would be the sample mean of the data. How does it relate to the normal distribution - could someone help?

Assume that we have some data π‘₯1,…,π‘₯π‘›βˆˆβ„x1,…,xn∈R. Our goal is to find a constant 𝑏b such that βˆ‘π‘–(π‘₯π‘–βˆ’π‘)2βˆ‘i(xiβˆ’b)2 is minimized.

  1. Find a analytic solution for the optimal value of 𝑏b.
  2. How does this problem and its solution relate to the normal distribution?
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I believe the optimal value of b is equal to the mean of the whole dataset which represents the Mean of a normal distribution. This makes (X_i - b) is the same as the exponent of e (X_i - mu)

In question 3 should the distribution be laplace or double exponential?

I found that the code can’t be run in COLAB because mxnet can’t be imported.