Introduction to Deep Learning Architectures

Convolutional Neural Network (CNN) A Convolutional Neural Network (CNN) is a type of artificial neural network designed to process and analyze data with grid-like topologies, such as images and videos. Imagine a CNN as a multi-layered filter that processes images to extract meaningful features and make predictions. Imagine you have a photograph of a handwritten digit, and you want a computer to recognize the digit. A CNN works by applying a series of filters over the image, gradually extracting more and more complex features. The first filters detect simple features like edges and lines, while later filters detect more complex patterns, such as shapes and digits. Convolutional Neural Network The layers of a CNN can be divided into three main types: convolutional layers, pooling layers, and fully connected layers. 1. Convolutional Layers: These layers apply filters, also known as kernels, to the image. Each filter slides over the image, computing a dot product between the filter and the pixels it covers. This process generates a new feature map, which highlights specific patterns in the image. The process is repeated multiple times with different filters, creating a set of feature maps that capture different aspects of the image. 2. Pooling Layers: Pooling layers perform a downsampling operation on the feature maps, reducing the spatial dimensions of the data while retaining important features. This helps to reduce computational complexity and prevent overfitting. The most common type of pooling is max pooling, which selects the maximum value from a small neighborhood of pixels. 3. Fully Connected Layers: These layers are similar to the layers in traditional neural networks. They connect every neuron in one layer to every neuron in the next layer. The output of the convolutional and pooling layers is flattened and passed through one or more fully connected layers, allowing the network to make a final prediction, such as recognizing the digit in the image. In summary, a CNN is a type of neural network designed to process grid-like data, such as images. It works by applying a series of filters, or kernels, to the image, gradually extracting more complex features. The output is then passed through pooling layers to reduce the spatial dimensions and prevent overfitting. Finally, the output is passed through fully connected layers to make a final prediction.