What are Convolutional Neural Networks?

Convolutional Neural Networks (CNNs) are a class of deep neural networks primarily used for analyzing visual imagery. They are composed of multiple layers, including convolutional layers, pooling layers, and fully connected layers. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input images. Here, are some components of CNN.

• Convolutional Layers: These layers apply convolution operations to the input, effectively extracting features like edges, textures, and patterns.
• Pooling Layers: Pooling layers reduce the spatial dimensions of the input by downsampling, helping to decrease computational complexity and control overfitting.
• Activation Functions: Activation functions like ReLU (Rectified Linear Unit) introduce non-linearity into the network, enabling it to learn complex relationships.
• Fully Connected Layers: Also known as dense layers, these layers perform classification based on the features extracted by previous layers.
• Loss Function: The loss function measures the difference between the predicted output of the network and the ground truth, guiding the network’s learning process.
• Optimization Algorithm: Algorithms like Stochastic Gradient Descent (SGD), Adam, or RMSprop are used to optimize the network’s parameters by minimizing the loss function.

Building a Convolutional Neural Network (CNN) using PyTorch involves several steps, including defining the architecture of the network, preparing the data, training the model, and evaluating its performance. In this article, we will see how we can build a CNN network in PyTorch.