**6. Definitions of CHC abilities**

These definitions are derived from an integration of the writings of Carroll (1993), Gustafsson and Undheim (1996), Horn (1991), McGrew (1997, 2005), and Schneider and McGrew (2012).

**7.1 Why CNN?**

neural network will be 28 � 28 � 3 ¼ 1872*:*.

*DOI: http://dx.doi.org/10.5772/intechopen.96324*

**7.2 Computer vision problem**

a neural network will be 3 million, which is pretty large.

in the weight matrix of the first layer will be, 3 billion.

Suppose we have 6 � 6 grayscale image.

We wish to detect vertical edges in it. So, the filter or kernel we use is as follows:

of the filter.

**137**

After convolution, the resultant matrix, we get as:

The filter can be learnt using neural networks, which will determine the 9 values

We treat each element of the filter as parameters and learn these parameters

�5 �40 8 �10 �22 3 0 �2 �4 �7 �3 �2 �3 �16

1 0 �1 1 0 �1 1 0 �1

using back-propagation, similar to the ordinary neural network.

**7.3 A short summary of convolutional operations**

Summary of convolutions

Suppose, we have a 28 � 28 RGB image. So, the total number of inputs in a

*Quest for I (Intelligence) in AI (Artificial Intelligence): A Non-Elusive Attempt*

Let we have a 1000 � 1000 RGB image. In this case the total number of inputs in

Since, the number of inputs have increased, the number of weight parameters, will also increase. If there are 1000 nodes in the first layer, the number of elements

We see that with the increase in the dimension of the image, there is a huge increase in the number of parameters, in a feedforward neural network. Thus, it is pretty difficult to train a neural network with such a large number of parameters.

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