2️⃣Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are a powerful class of neural networks commonly used for image recognition and computer vision tasks. CNNs have revolutionized the field of computer vision by achieving state-of-the-art results on various image recognition tasks.

In this article, we will provide an overview of CNNs, their architecture, and how they work.

What is a Convolutional Neural Network?

A Convolutional Neural Network is a type of artificial neural network that is designed to process and classify images. CNNs are inspired by the way the visual cortex in the brain processes visual information. Like other neural networks, CNNs consist of interconnected neurons that are trained on labeled data.

CNNs are composed of several layers of neurons. The first layer of a CNN is typically a convolutional layer. This layer applies filters to the input image to extract features such as edges, corners, and textures. The output of the convolutional layer is passed through a non-linear activation function such as ReLU (Rectified Linear Unit), which introduces non-linearity into the network.

After the activation function, the output is passed through a pooling layer, which reduces the spatial size of the input image. Pooling is typically done using max pooling, where the maximum value in each sub-region is selected as the output.

The output of the pooling layer is passed through several additional convolutional and pooling layers. Finally, the output of the last pooling layer is passed through one or more fully connected layers, which perform the classification task.

Architecture of a Convolutional Neural Network

A typical CNN consists of several layers, each of which performs a specific operation on the input image. The most common layers in a CNN are:

Convolutional Layer

The convolutional layer is the first layer of a CNN. It applies a set of filters to the input image to extract features such as edges and textures. Each filter is a small matrix that slides over the input image, computing a dot product between the filter weights and the input pixels at each location. The output of the convolutional layer is a set of feature maps, where each map represents a different feature of the input image.

Activation Layer

The activation layer introduces non-linearity into the network by applying an activation function to the output of the convolutional layer. The most common activation function used in CNNs is the ReLU (Rectified Linear Unit) function, which sets all negative values to zero and leaves positive values unchanged.

Pooling Layer

The pooling layer reduces the spatial size of the input image by selecting the maximum value in each sub-region. The most common pooling operation used in CNNs is max pooling, where the maximum value in each sub-region is selected as the output.

Fully Connected Layer

The fully connected layer is the final layer of a CNN. It performs the classification task by mapping the output of the previous layer to a set of class scores. The fully connected layer is typically composed of multiple layers of neurons.

How does a Convolutional Neural Network work?

The key to the success of CNNs is their ability to automatically learn and extract relevant features from images. This is achieved by training the network on a large dataset of labeled images.

During the training process, the network learns to adjust the weights of the filters in the convolutional layers to minimize the classification error. This is done by iteratively updating the weights using an optimization algorithm such as stochastic gradient descent (SGD).

Once the network is trained, it can be used to classify new images by passing the image through the network and computing the output of the last fully connected layer. The output of the fully connected layer represents the probability of the input image belonging to each of the classes in the dataset.