<p>In general, the higher the model complexity, the more training examples (and computation time) is required for the model to learn.</p>
<p>In general, network size is directly proportional to model complexity. The larger the network size, the more parameters, thus higher the complexity.</p>

CC BY-SA 4.0
2023-06-19T04:40:28.040Z
2023-06-19T04:40:28.040Z
lpounng
<p>What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?</p>

CC BY-SA 4.0
2023-06-16T02:11:50.803Z
Please name one such measure of model complexity, so we can elaborate from there.
lpounng
CC BY-SA 4.0
2023-06-16T09:28:05.970Z
Let us use VC complexity.
Justaperson
<deep-learning><training><models><sample-complexity>
What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?
Justaperson
# What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?

(Asked by: username_0 on 2023-06-15)

What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?

**Tags:** deep-learning, training, models, sample-complexity

> **Comments:**
> **username_1**: Please name one such measure of model complexity, so we can elaborate from there.  
> **username_0**: Let us use VC complexity.  

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## 1 answers

### Answer by username_1 on 2023-06-19. Score: 0

In general, the higher the model complexity, the more training examples (and computation time) is required for the model to learn.

In general, network size is directly proportional to model complexity. The larger the network size, the more parameters, thus higher the complexity.


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