Deep Learning.pptx
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Convolutional neural networks (CNNs) are commonly used for image classification and recognition tasks. CNNs use convolutional layers that apply learnable filters to detect patterns in input images. The filters are convolved across the width and height of the input to produce an activation map. Padding is added to images processed by CNNs to allow filters to fully cover edge pixels and produce accurate analyses. The dimensions of convolved outputs depend on the input and filter sizes, as well as any padding used.
Deep Learning.pptx
- 2. CONVOLUTIONAL NEURAL NETWORK Convolutional Neural Network is one of the main categories to do image classification and image recognition in neural networks. Scene labeling, objects detections, and face recognition, etc., are some of the areas where convolutional neural networks are widely used.
- 4. CONVOLUTIONAL LAYER Computers read images as pixels and it is expressed as matrix (NxNx3) – (height by width by depth) The Convolutional layer makes use of a set of learnable filters. A filter is used to detect the presence of specific features or patterns present in the original image (input). It is usually expressed as a matrix, with a smaller dimension but the same depth as the input file. This filter is convolved across the width and height of the input file and a dot product is computed to give an activation function map.
- 5. FILTER Convolution filters are filters (multi- dimensional data) used in Convolution layer which helps in extracting specific features from input data. There are different types of Filters like Gaussian Blur, Prewitt Filter, Sobel Filter, Canny Edge Detector and many more which we have covered along with basic idea.
- 6. WHAT ARE FILTERS/KERNELS? A filter provides a measure for how close a patch or a region of the input resembles a feature. A feature may be any prominent aspect – a vertical edge, a horizontal edge, an arch, a diagonal, etc.
- 7. DIMENSIONS OF THE CONVOLVED OUTPUT? If the input image size is ‘n x n’ & filter size is ‘f x f ‘, then after convolution, the size of the output image is: (Size of input image – filter size + 1)
- 8. PADDING Padding basically extends the area of an image in which a convolutional neural network processes. The kernel/filter which moves across the image scans each pixel and converts the image into a smaller image. In order to work the kernel with processing in the image, padding is added to the outer frame of the image to allow for more space for the filter to cover in the image. Adding padding to an image processed by a CNN allows for a more accurate analysis of images.
- 10. THERE ARE THREE TYPE PADDING Same padding Causal padding Valid padding
- 11. SAME PADDING In this type of padding, the padding layers append zero values in the outer frame of the images or data so the filter we are using can cover the edge of the matrix and make the inference with them too.
- 12. VALID PADDING This type of padding can be considered as no padding.
- 13. CAUSAL PADDING This is a special type of padding and basically works with the one-dimensional convolutional layers.
- 14. STRIDES When the array is created, the pixels are shifted over to the input matrix. The number of pixels turning to the input matrix is known as the strides. When the number of strides is 1, we move the filters to 1 pixel at a time. Similarly, when the number of strides is 2, we carry the filters to 2 pixels, and so on.