Learnable parameters in DNN I've come across the term &quot;learnable parameters&quot; recently, and googling didn't help much as most search was describing learnable parameters in a CNN instead of a DNN. Is there any difference between the two?
How would I compute the number of learnable parameters in a DNN? Could anyone please explain what those are with an example? I'm new to machine learning so I would appreciate some help on this.
In neural networks in general, and in deep learning algorithms (CNN, DNN, etc.) that are also based on neural networks, learnable parameters are parameters that will be learned by the model during the training procedure such as weights and biases.
You can generate learnable parameters for each layer of your model.
DNN is a deep neural network. What I understand that when a neural network becomes deep, it can be said that it's a deep learning model.
So, to compute a DNN or CNN learnable parameters, you can build your model using Keras. It can automatically generate the models learnable parameters for each layer. When you complete building your model, type model.summary(). It generates a list of learnable parameters when you run it.
This link builds a DNN model using using Keras.