What is the role of the fully connected layer in a CNN?
The fully connected layer, also known as the dense layer, plays a crucial role in convolutional neural networks (CNNs) and is an essential component of the network architecture. Its purpose is to capture global patterns and relationships in the input data by connecting every neuron from the previous layer to every neuron in the fully
How do we prepare the data for training a CNN model?
To prepare the data for training a Convolutional Neural Network (CNN) model, several important steps need to be followed. These steps involve data collection, preprocessing, augmentation, and splitting. By carefully executing these steps, we can ensure that the data is in an appropriate format and contains enough diversity to train a robust CNN model. The
What is the purpose of backpropagation in training CNNs?
Backpropagation serves a crucial role in training Convolutional Neural Networks (CNNs) by enabling the network to learn and update its parameters based on the error it produces during the forward pass. The purpose of backpropagation is to efficiently compute the gradients of the network's parameters with respect to a given loss function, allowing for the
How does pooling help in reducing the dimensionality of feature maps?
Pooling is a technique commonly used in convolutional neural networks (CNNs) to reduce the dimensionality of feature maps. It plays a crucial role in extracting important features from input data and improving the efficiency of the network. In this explanation, we will delve into the details of how pooling helps in reducing the dimensionality of
What are the basic steps involved in convolutional neural networks (CNNs)?
Convolutional Neural Networks (CNNs) are a type of deep learning model that have been widely used for various computer vision tasks such as image classification, object detection, and image segmentation. In this field of study, CNNs have proven to be highly effective due to their ability to automatically learn and extract meaningful features from images.