How can a CNN account for spectro-temporal constraints in neural data?

1. Is the best neural network model for temporal data?
2. Can we use CNN for sequential data?
3. How does CNN calculate trainable parameters?
4. What are the limitations of CNN?

Is the best neural network model for temporal data?

1 Answer. The correct answer to the question “What is the best Neural Network model for temporal data” is, option (1). Recurrent Neural Network. And all the other Neural Network suits other use cases.

Can we use CNN for sequential data?

A CNN can be instantiated as a Sequential model because each layer has exactly one input and output and is stacked together to form the entire network.

How does CNN calculate trainable parameters?

CONV layer: This is where CNN learns, so certainly we'll have weight matrices. To calculate the learnable parameters here, all we have to do is just multiply the by the shape of width m, height n, previous layer's filters d and account for all such filters k in the current layer.

What are the limitations of CNN?

Some of the disadvantages of CNNs: include the fact that a lot of training data is needed for the CNN to be effective and that they fail to encode the position and orientation of objects. They fail to encode the position and orientation of objects. They have a hard time classifying images with different positions.