Dear Bhanu,

I suggest you to see links and attached files on topic.

Comparing Shallow versus Deep Neural Network Architectures for ...

ceur-ws.org/Vol-1734/fmt-proceedings-2016-paper2.pdf

On the complexity of shallow and deep neural network ... - UCL/ELEN

https://www.elen.ucl.ac.be/Proceedings/esann/.../es2014-44.pdf

Deep vs. Shallow Networks: an Approximation Theory Perspective ...

https://arxiv.org/pdf/1608.03287

Learning Functions: When Is Deep Better Than Shallow arXiv ...

https://arxiv.org/pdf/1603.00988

Why and When Can Deep-but Not Shallow-networks Avoid the Curse ...

https://cbmm.mit.edu/.../art%253A10.1007%252Fs11633-017-105...

Best regards

Hi,

you probably want to read these two studies :-)

https://pdfs.semanticscholar.org/f594/f693903e1507c33670b89612410f823012dd.pdf

https://pdfs.semanticscholar.org/4614/0d548991b4fc9d03dcc412d398bf9b17dae9.pdf

B.R.,

Ari

Besides an input layer and an output layer, a neural network has intermediate layers, which might also be called hidden layers. They might also be called encoders.

A shallow network has less number of hidden layers. While there are studies that a shallow network can fit any function, it will need to be really fat. That causes the number of parameters to increase a lot.

There are quite conclusive results that a deep network can fit functions better with less parameters than a shallow network.

Besides an input layer and an output layer, a neural network has intermediate layers, which might also be called hidden layers. They might also be called encoders.

A shallow network has less number of hidden layers. While there are studies that a shallow network can fit any function, it will need to be really fat. That causes the number of parameters to increase a lot.

There are quite conclusive results that a deep network can fit functions better with less parameters than a shallow network.

Besides an input layer and an output layer, a neural network has intermediate layers, which might also be called hidden layers. They might also be called encoders.A shallow network has less number of hidden layers. While there are studies that a shallow network can fit any function, it will need to be really fat. That causes the number of parameters to increase a lot.

If you have a single layer, the number of neurons you need, to match a multi-layer network, is combinatorially large.

With multiple layers, each layer can create more and more abstract features/concepts. Like, in our brain, the output from our eyes enters the brain at the back, and passes through very low-level feature detectors. Lines and such. As the signal moves forward through the layers, the features become more abstract, from simple line detectors to various types of moving object and so on.

So it is with nets. Depth gives the possibility of abstracting relatively abstract concepts, in the upper layers, which massively improves the ability of the network to classify and so on. As long as there is sufficient data.

I request you to see link.

https://pdfs.semanticscholar.org/f594/f693903e1507c33670b89612410f823012dd.pdf

In short, "shallow" neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types.

There are papers that highlight that deep NN with the right architectures achieve better results than shallow ones that have the same computational power (e.g. number of neurons or connections).

The main explanation is that the deep models are able to extract/build better features than shallow models and to achieve this they are using the intermediate hidden layers.

In short, "shallow" neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types.

There are papers that highlight that deep NN with the right architectures achieve better results than shallow ones that have the same computational power (e.g. number of neurons or connections).

In short, "shallow" neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types. There are papers that highlight that deep NN with the right architectures achieve better results than shallow ones that have the same computational power (e.g. number of neurons or connections).

Both shallow and deep networks are capable of approximating any function. For the same level of accuracy, deeper networks can be much more efficient in terms of computation and number of parameters.

Besides an input layer and an output layer, a neural network has intermediate layers, which might also be called hidden layers. They might also be called encoders.

A shallow network has less number of hidden layers. While there are studies that a shallow network can fit any function, it will need to be really fat. That causes the number of parameters to increase a lot.

There are quite conclusive results that a deep network can fit functions better with less parameters than a shallow network.

In short, "shallow" neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types.

Shallow Network vs deep network

In short, "shallow" neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types. ... In lay man terms : Shallow means : NOT DEEP that is no of hidden layer = 1 .

In short, "shallow" neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types. ... In lay man terms : Shallow means : NOT DEEP that is no of hidden layer = 1

In short, "shallow" neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types. ... In lay man terms : Shallow means : NOT DEEP that is no of hidden layer = 1

For the same level of accuracy, deeper networks can be much more efficient in terms of computation and number of parameters. Deeper networks are able to create deep representations, at every layer, the network learns a new, more abstract representation of the input.

In short, "shallow" neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types. ... In lay man terms : Shallow means : NOT DEEP that is no of hidden layer = 1

A shallow network has less number of hidden layers. While there are studies that a shallow network can fit any function, it will need to be really fat. That causes the number of parameters to increase a lot.

There are quite conclusive results that a deep network can fit functions better with less parameters than a shallow network.

A shallow network has less number of hidden layers. While there are studies that a shallow network can fit any function, it will need to be really fat. That causes the number of parameters to increase a lot.

There are quite conclusive results that a deep network can fit functions better with less parameters than a shallow network.

A shallow network has less number of hidden layers. While there are studies that a shallow network can fit any function, it will need to be really fat. That causes the number of parameters to increase a lot.There are quite conclusive results that a deep network can fit functions better with less parameters than a shallow network.

Shallow neural networks is a term used to characterize NNs with just

one hidden layer as opposed to deep NNs with multiple hidden layers.

Hassans answer makes a lot of sense.

Shallow network just does not have that much "explaining power" than deep networks. The challenge with deep network is that it does not large enough training set to get it work as wanted.

B.R.,

Ari