November 2018
Intermediate to advanced
310 pages
7h 54m
English
Content preview from Hands-On GPU Programming with Python and CUDA
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Chapter 9, Implementation of a Deep Neural Network
- One problem could be that we haven't normalized our training inputs. Another could be that the training rate was too large.
- With a small training rate a set of weights might converge very slowly, or not at all.
- A large training rate can lead to a set of weights being over-fit to particular batch values or this training set. Also, it can lead to numerical overflows/underflows as in the first problem.
- Sigmoid.
- Softmax.
- More updates.
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