Hi guys,

I was wondering if any of you understand the step from 5.38 → 5.39 on page 123?

I thought the definition of Var[X] was simply:

Hi guys,

I am trying to follow derivation 5.73 → 5.76 on Baysian linear regression.

Starting from the Book:

Hi, I have a question about sentences of the page 102 of chapter 5.1.3. Regarding the formula 5.1, it says: this decomposition means that we can solve the ostensibly unsupervised problem of mo...

[1,-1] is not in span of [1,1] and [3,3].

Hi, I have a question on this chapter 2, pg 41 second Paragragh starting with "While any real symmetric matrix .."

Here the author says if there are two or more eigenv...

Because the decoding function is not necessarily linear. Check kernel PCA.

hi everyone, just joined the community. Excited to be here and learn more about this field.

Is it only me but wouldn't a quadratic function better fit the data points than the linear regression line?

I've always wondered about deep learning's history, and had read in several articles that neural networks and back-propagation used to be popular in 1980s. Didn't know that there was another deep learning phase in the 1960s!

I am confused by equation 3.28 in section 3.9.5, used to define an empirical distribution. According to the text, when dealing with discrete variables, p(x) is simply the frequency of that val...

Hello ,all.

I have a question with the "3.9 mixture model", I have no idea to understand the caption of figure 3.2 .

http://www.deeplearningbook.org/contents/prob.html#pf10

Section 3.8: "Expectation, Variance and Covariance" the example in the final lines in the section

I have three questions:

In softmax equation 4.1, the book says:

Consider what happens when all the xi are equal to some constantc. Analytically,we can see that all the outputs should be...

Since shape(D) is (n,l), and transpose of D is (l,n), after left multiply D with its transpose, the output shape will be(l,l), and the constraint of all columns of D are orthonormal is essential if you wanna simplify the matrix myltiplication