Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with neural-network
Search options not deleted
user 39929
Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.
6
votes
1
answer
4k
views
Proper derivation of dz[1] expression for backpropagation algorithm
For backpropagation algorithm, is it true to have weight, $w$ transposed in the expression of $dz^{[1]} = w^{[2]T}dz^{[2]} * g^{[1]'}(z^{[1]}) $ ? Could anyone show me why $w^{[2]T}$ instead of just $ …
- 283
1
vote
0
answers
155
views
Winograd Convolution
For https://www.intel.ai/winograd-2/ , why use stride = 2 ?
Why need to transform input image pixels ?
Why this C++ implementation of winograd convolution does not require any input tensors transfor …
- 283
1
vote
0
answers
86
views
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
Could anyone help to derive equation (15) dL/dx in L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks ?
I found that the term inside the rectangle for the expression (12) is e …
- 283