Concept of deep learning is to mimic the human brain.
Weights represent the long-term memory of a neural network, weights of ANN go
into Temporal Lobe (responsible for long term memory).
CNN: Related to vision, recognition of images/objects which is Occipital
lobe.
RNN: Short term memory, just happened which is the Frontal Lobe.
Sensation and perception are taken care by Parietal Lobe (NN is yet to
create in this zone).
FCNN & CNN:
Below are some of the properties of
FCNN and CNN
1.The output at any time step is
independent of the previous layer input/output
2.The input was always of the
fixed-length/size for ex. for FCNN all the input instances had the
same let’s say ‘100’ input
features whereas in case of CNN's let’s say all the input images are of size ‘30 X 30’ or if of different
size, then we can rescale the input image to the required/appropriate
dimension.
3. all the neurons in any of the layers are connected to all the neurons in
the previous layer
