Hyperparameter Tuning
Explore the process of hyperparameter tuning to improve convolutional neural network performance. Understand how adjusting learning rate, batch size, number of epochs, optimizers, and loss functions affects training outcomes. Gain practical knowledge in modifying these parameters one at a time to observe their impact and optimize model accuracy effectively.
In CNNS, hyperparameter tuning involves optimizing different parameters to enhance model performance.
Learning rate
The learning rate determines the size of the steps taken to update the model parameters during training. It affects the speed at which the model learns and improves. A lower learning rate means slower learning but helps the model capture smaller details in the data. In contrast, a larger learning rate speeds up training but increases the risk of the model overshooting the best solution.
Batch size
The batch size is the number of training examples or samples processed during training when the data is divided into smaller subsets or batches of 64, 128, or 256. Each batch is processed independently through the network, and the network weights are updated based on the computed gradients for that batch. The batch size affects how the model learns ...