Hyperparameter Tuning
Learn about the fundamentals of hyperparameter tunning and learn about parameters such as learning rate, batch size, number of epochs, optimizer, and loss function.
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 and how quickly it trains. A larger batch size can make training faster because multiple examples are processed at once. However, it also requires more memory. On the other hand, using a smaller batch size can help the model generalize better and prevent overfitting. It allows the model to learn from different examples more frequently. That said, training can be slower because only a few images are processed at a time.
Number of epochs
The number of epochs ...