Fundamentals Of Convolutional Neural Networks Training Ppt

Slide 1

This slide gives an overview of Convolutional Neural Networks. ConvNet, is a deep learning network design that learns from data without the requirement for human feature extraction. CNNs are beneficial for recognizing objects, faces, and settings by looking for patterns in images.

Slide 2

This slide describes how Convolutional Neural Networks work. CNNs are divided into three layers that are convolutional layer, pooling layer, and fully-connected layer.

Slide 3

This slide depicts the Convolutional Layer in a Convolutional Neural Network. The majority of the computation takes place in the convolutional layer of a CNN. This layer requires input data, a filter, and a feature map.

Slide 4

This slide describes hyperparameters of the Convolution layer in a CNN. These parameters are number of filters, stride, and zero-padding which is further divided into valid padding, same padding, and full padding.

Instructor’s Notes: 

• Number of filters: The depth of the output is determined by the amount of filters used. Three distinct filters, for instance, would result in three distinct feature maps, resulting in a depth of three
• Stride: The stride of the kernel is the number of pixels traversed over the input matrix. Despite the fact that stride values of two or more are unusual, a longer stride means less output
• Zero-padding: Zero-padding is used when the filters don't fit the input image. All members outside the input matrix are set to zero, resulting in a larger or equal-sized output. Padding is of three types
• Valid padding: This is also referred to as "no padding." If the dimensions do not align, the last convolution is discarded
• Same padding: This padding guarantees that the size of the output layer and input layer is the same
• Full padding: This type of padding enhances the output's size, By padding the input's border with zeros

Slide 5 

This slide depicts the Pooling Layer in a Convolutional Neural Network. Downsampling, also known as pooling layers, reduces the number of parameters in the input by reducing dimensionality. Max Pooling and Average Pooling are its two types.

Instructor’s Notes: The pooling process sweeps a filter across the entire input, similar to the convolutional layer, except that this filter has no weights. Instead of populating the output array with values from the receptive field, the kernel uses an aggregation function.

• Max Pooling: The filter chooses the pixel with the highest value to transmit to the output array as it advances across the input. In comparison to average pooling, this strategy is employed more frequently
• Average Pooling: The average value inside the receptive field is determined as the filter passes over the input and is sent to the output array

Slide 6 

This slide depicts the Fully-Connected Layer in a Convolutional Neural Network. Each output layer node connects directly to a node in the preceding layer in the fully-connected layer. This layer performs categorization based on the features extracted by the preceding layers and their filters.