Basic Introduction to Convolutional Neural Network.pptx

Basic introduction on convolutional Neural network

1. Convolutional Neural Networks (CNN) 

2. Motivation Let us assume that we want to create a neural network model that is capable of recognizing swans in images. A swan has certain characteristic features that can be used for recognition purposes.

3. Motivation For some images, it may be more difficult to determine whether a swan is present . Harder to classify swan image.

4. Drawback of MLPs Do not scale well for images Ignore the information brought by pixel position and correlation with neighbors Cannot handle translations Solution: CNN

5. Convolutional Neural Networks “A simple CNN is a sequence of layers, and every layer of a CNN transforms one volume of activations to another through a differentiable function .” Each layer in CNN is associated with converting the information from the values, available in the previous layers, into some more complex information and pass on to the next layers for further generalization.

6. Convolutional Neural Network Architecture A CNN typically has three layers: Convolutional Layer, Pooling Layer and Fully Connected Layer. Each of these layers has different parameters that can be optimized and performs a different task on the input data. The layers are arranged in such a way so that they detect simpler patterns first (lines, curves, etc.) and more complex patterns (faces, objects, etc.) further along.

7. Convolution The Convolutional Layer is related to feature extraction . In this layer, filters are applied to the original image, or to other feature maps in a deep CNN. Filters or ‘kernels’ are also an image that depict a particular feature. It is a matrix which extracts features from an image (this is where the learning takes place).

8. Padding Padding essentially makes the feature maps produced by the filter kernels the same size as the original image. 

9. Pooling After the image is passed through the first filter, it will then go on to the pooling layer. The pooling layer reduces the size of the filter layer, which allows us to train the model faster. It also prevents overfitting by dropping unwanted values in the filter.

10. Re-Lu Re-Lu (Rectified Linear Unit) is a common activation function Re-Lu will only return values if the value is greater than zero, helping decrease the training time. The Re-Lu Activation function

11. Fully Connected Layers Now that all the features of the image have been extracted by the Convolutional layers, next step is to classify the images. In a fully connected layer, every neuron shares a connection with all the other neurons in the layer before and after it.

12. Applications