Epochs

Definition: An epoch refers to one complete pass through the entire dataset during training. In this case, the dataset will be passed through the neural network 100 times.

Typical Range: Epochs can range from **10 to 1000** depending on the dataset size and complexity. Lower values (e.g., **10-50**) are usually used for smaller datasets or faster training, while higher values (e.g., **100-1000**) are used for more complex data.

• When increased: Training for more epochs gives the model more time to learn, but too many can cause overfitting, where the model becomes too specialized on the training data.
• When decreased: Fewer epochs will make training faster, but the model might not learn as much, potentially missing important patterns.

Batch Size

Definition: The batch size determines how many samples are processed at once before updating the model's internal parameters. Here, the batch size is 8, meaning the model processes 8 entries at a time.

Typical Range: Batch sizes typically range from **8 to 256**.

• When increased: A larger batch size allows faster training but may overlook finer details in the data.
• When decreased: A smaller batch size means more precise learning, but it takes longer to train and can result in noisier updates.

Dropout Rate

Definition: The dropout rate is a regularization method to prevent overfitting by randomly "dropping" a percentage of neurons during training. A 40% dropout rate means that 40% of neurons are turned off during each training step.

Typical Range: The dropout rate typically ranges from **0.2 to 0.5**.

• When increased: A higher dropout rate reduces the risk of overfitting but may limit the model's ability to learn complex patterns.
• When decreased: A lower dropout rate allows the model to learn more from the data but increases the risk of overfitting.

What is Overfitting?

Overfitting happens when a model becomes too good at learning from the training data, memorizing it rather than understanding general patterns.