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Chapter 6: Deep Feedforward Networks

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 Timothee Cour.

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http://deeplearningbook.org/contents/mlp.html

Category: Deep Learning Book

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Herli Menezes39w

there is a very good explanation at StackExchange given by Gabrriel Romon Motivating sigmoid output units in neural networks starting with unnormalized log probabilities linear in $z=w^Th+b$ and $\phi(z)$

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equation 6.22 can be rewritten into:

\frac{e^{yz}}{e^{yz}+e^{(1-y)z}}

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Read (6 words)

How to understand the equation 6.31 , and How does it derive from ?

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Read (14 words)

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2.

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Chapter 10: Sequence Modeling - Recurrent and Recursive Nets

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 Ian Goodfellow.

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http://deeplearningbook.org/contents/rnn.html

Category: Deep Learning Book

3.

discussion

CHAPTER 6: Confused by Algorithm 6.4

I am confused by this line:

$\nabla_{W^{(k)}}J = gh^{(k-1)T}+\lambda\nabla_{W^{(k)}}{\Omega(\theta)}$

This equation is derived by the equation below:

$a^{(k)}= b^{(k)}+W^{(k)}h^{(k-1)}$

But the gradient of $W^{(k)}h^{(k-1)}$ with respect to $W^{(k)}$ should be $h^{(k-1)}$, right?

Why is there a transpose $T$ here?

A matrix calculus problem in backpropagation encount...

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A matrix calculus problem in backpropagation encountered when studying Deep Learning

https://math.stackexchange.com/questions/2970202/a-matrix-calculus-problem-in-backpropagation-encountered-when-studying-deep-lear

Category: Deep Learning Book

4.

discussion

Chapter 8: Optimization for Training Deep Models

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

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http://deeplearningbook.org/contents/optimization.html

Category: Deep Learning Book

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Chapter 7: Regularization for Deep Learning

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

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http://deeplearningbook.org/contents/regularization.html

Category: Deep Learning Book

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Michael Bilow2y

In Chapter 7, Page 234, it says:

The new matrix is the same as the original one, but ...

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Chapter 9: Convolutional Networks

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

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http://deeplearningbook.org/contents/convnets.html

Category: Deep Learning Book

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I don't think so - the indices taken alone can be arbitrary (i.e. he could have written a,b,c or x,y,z for the indices of V). However when we are taking sums we need to ensure that the indices match.

In other words, I can say that V_x,y,zrepresent the ...

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Could someone explain what the last sentence on page 342 means:

"In linear algebra notation, we index into arrays using 1 for the first entry"

I'm rather confused, because say if I wanted to find...

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Bastian Lampe2y

This is on page 342. In the first sentenced, j is referred to as a channel in the input. In the second sentence, j is refe...

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