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Deep Learning Book

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1.

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How to derive equation (6.23) from (6.22) ?by Fan Li

I have a question about the sigmoid function as the question.

I wish some help on this problem.

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2.

discussion

Equation 19.45.by Rishabh Gupta

Can anyone explain how to get the equivalent form of eqn 19.44, given in 19.45?

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3.

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Chapter 2, Section 2.12 Page 47by Abhinaw Raj

After Equation 2.67, the new paragraph says that since we're using the same matrix D to decode all the points, we can no longer consider the points in isolation. Then the author uses the Frobenius norm of the matrix instead of the L2 norm used before. So, why is that?

Also, what are the limits of summation in the norm and how are we defining the transformation on a scalar, ie r(x⁽ⁱ⁾)_j

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NaN.

discussion

How to derive equation (6.23) from (6.22) ?by Fan Li

I have a question about the sigmoid function as the question.

I wish some help on this problem.

Read more…(18 words)

comment in this discussion

P(y) = exp(y*z)/SUM[y'=0..1](y'*z) = exp(y*z)/(exp(0*z)+exp(1*z)) = exp(y*z)/(1+exp(z)) 6.22

P(y) = Sigmoid((2*y-1)*z) 6.23

where

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NaN.

discussion

Chapter 2: Linear Algebraby Keshav Dhandhania

If you're reading Deep Learning Book, feel free to ask questions, discuss the material or share resources.

Below is a video from a book club in San Francisco, USA discussing this chapter. Presented by Gavin Crooks.

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comment in this discussion

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

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NaN.

discussion

Chapter 6, Section 6.2.2.3 Page 187by Lalit Kumar

A line in first paragraph says "Defining P(y=1|x)=sigmoid(z) is equivalent to defining P(y=1|x) = softmax(z)1 with two-dimensional z and z1=0."

Could anybody help me understand this line?

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comment in this discussion

Ya sure. In probablity theory, the sum of all possible outcomes sum over to 1. For example a coin(fair or not) can only land on two sides, heads and tails. So in neural networ if there are only two possible states 0 and 1 the probablity of them occuring add up to 1.

So if prob(1) is say X, the prob(0) would be 1-x.

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