How do we prepare the training data for a CNN?
Preparing the training data for a Convolutional Neural Network (CNN) involves several important steps to ensure optimal model performance and accurate predictions. This process is important as the quality and quantity of training data greatly influence the CNN's ability to learn and generalize patterns effectively. In this answer, we will explore the steps involved in
What is the purpose of normalizing data before training a neural network?
Normalizing data before training a neural network is an essential preprocessing step in the field of artificial intelligence, specifically in deep learning with Python, TensorFlow, and Keras. The purpose of normalizing data is to ensure that the input features are on a similar scale, which can significantly improve the performance and convergence of the neural
Why is data normalization important in regression problems and how does it improve model performance?
Data normalization is a important step in regression problems, as it plays a significant role in improving model performance. In this context, normalization refers to the process of scaling the input features to a consistent range. By doing so, we ensure that all the features have similar scales, which prevents certain features from dominating the