Convolutional Neural Networks and their Applications
Convolutional Neural Networks (CNNs) is a type of deep learning models that are widely used in image and video analysis tasks. They have been used for a wide range of applications, such as image classification, object detection, image segmentation, and many others.
In this tutorial, we will learn about the basics of CNNs and some of their applications in TensorFlow.
What is a Convolutional Neural Network?
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A Convolutional Neural Network (CNN) is a type neural network in deep learning that has been specifically designed to process image data.
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The key feature of CNNs is the use of convolutional layers.
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Convolutional layers are designed to identify and learn spatial patterns in the input data.
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In a CNN, convolutional layers are followed by pooling layers, which reduce the spatial dimensions of the output, and fully connected layers, which perform the final classification.
How do Convolutional Layers work?
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Convolutional layers are the building blocks of CNN in TensorFlow.
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They work by applying a set of filters to the input data.
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Each filter is a small matrix of weights that is used to scan over the input data. At each position, the filter performs a dot product between itself and a small region of the input data. This produces a single output value, which is placed into the output feature map at the corresponding position.
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By applying many filters in parallel, the convolutional layer is able to learn a set of features that are useful for performing the task.
How do Pooling Layers work?
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Pooling layers are used to reduce the spatial dimensions of the output produced by convolutional layers.
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The most common form of pooling is max pooling, where the maximum value in each local region of the feature map is selected and placed into the output.
How do Fully Connected Layers work
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Fully connected layers are used to perform the final classification in a CNN. They take input of features that has been flattened from the output of the preceding layers.
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The fully connected layers then apply a set of weights to this vector, and produce a set of output values.
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The output values are then passed through a softmax function, which produces a probability distribution over the classes.
Applications of Convolutional Neural Network
CNNs have been used for a wide range of applications in image and video analysis. Some of the most common applications are:
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Image Classification
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Object Detection
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Image Segmentation
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Face Recognition
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Image Generation
In upcoming Tutorial, we’ll practically implement an Convolutional Neural Network.
