Convolutional Neural Networks (CNNs) are a specialized kind of neural network that is specifically suited to processing data that has a grid-like topology. They are especially effective for tasks like image recognition, object detection, and other tasks related to visual perception. CNNs are named after the mathematical operation “convolution,” which is central to their functionality.
While CNNs were initially developed for image processing, where data can be represented as a 2D pixel grid, CNNs can also be applied to other types of data, such as speech, represented as a 1D array of audio waveform data.
CNNs were designed to mimic the way the human brain perceives visual information. They were inspired by biological processes, namely, the connectivity pattern between neurons resembles the organization of the animal visual cortex.
Here are some important components and concepts related to CNNs:
- Convolutional layers: This is the fundamental building block of CNNs. In a convolutional layer, a small window (or filter) scans across an image, performing element-wise multiplication with the section of the image it currently overlays, and then summing those results to get a single output pixel in the resultant feature map. This process is repeated for every location the filter can reach on the input, producing a complete feature map. These filters are learned during the training process and can end up capturing complex patterns in the data.
- Pooling layers: These are usually used immediately after convolutional layers. Pooling layers reduce the spatial dimensions (width, height) of the input by applying a down-sampling operation along the spatial dimensions. This makes the network less sensitive to the exact location of features in the image and helps to control overfitting. A common pooling operation is max pooling, where the maximum value is selected from a small window.
- ReLU (Rectified linear units): CNNs typically use ReLU as their activation function to introduce non-linearity into the model. ReLU works by setting all negative pixel values to zero. This makes the model capable of learning and capturing complex patterns in the data.
- Fully connected layers: Towards the end of a CNN, there are usually one or more fully connected layers (also called dense layers). These layers take the high-level features produced by the convolutional layers and pooling layers, which are flattened into a one-dimensional vector, and learn to classify them into different categories.
- Backpropagation and gradient descent: CNNs are trained using backpropagation and gradient descent, which are standard techniques in deep learning. These methods adjust the weights and biases of the filters to minimize the difference between the model’s predictions and the actual values (the loss).
- Feature hierarchy: One key aspect of CNNs is their ability to learn a hierarchy of features. Lower layers in the network tend to learn simple, low-level features like edges and colors, while deeper layers learn increasingly complex and abstract features.
The architecture of a CNN is designed to take advantage of the 2D structure of an input image (or other 2D input such as a speech signal). This is achieved with local connections and tied weights followed by some form of pooling which results in translation-invariant features. Another benefit of CNNs is that they are easier to train and have many fewer parameters than fully connected networks with the same number of hidden units.
CNNs are the basis for many modern advances in computer vision, such as real-time object detection and recognition systems, face recognition systems, and even in medical imaging analysis. They are also used in other domains where spatial structure matters, such as processing natural language or time series data.