Chapter 1: Introduction to Hyperparameters

When delving into deep learning (DL) algorithms, one of the significant hurdles is the adjustment and management of hyperparameter values. This process, known as hyperparameter tuning or optimization, is crucial as hyperparameters dictate various aspects of DL algorithms.

Hyperparameters influence the time and computational resources needed for algorithm execution. They help shape the neural network model and significantly impact its predictive accuracy and ability to generalize. Essentially, hyperparameters govern the behavior and architecture of neural network models, making it vital to understand their roles and classifications.

Important Facts About Hyperparameters

Before diving into the specifics of neural network hyperparameters, it’s essential to highlight some key distinctions:

• Parameters vs. Hyperparameters: While both are variables in machine learning (ML) and DL algorithms, they differ significantly. Parameters are values learned from data during training—such as weights and biases—which are optimized through backpropagation. In contrast, hyperparameters are predefined by the ML engineer prior to training and are not automatically derived from data. Adjusting these values is crucial for developing effective models, as they influence the model's structure and performance.

Classification Criteria for Hyperparameters

Neural network hyperparameters can be categorized based on three main criteria:

1. Network Structure
2. Learning and Optimization
3. Regularization Effect

#### Hyperparameters Affecting Network Structure

The hyperparameters under this category directly shape the neural network's architecture.

• Number of Hidden Layers: This aspect, often referred to as the network's depth, is crucial for determining the model's learning capacity. A sufficient number of hidden layers is necessary to capture essential non-linear patterns within complex datasets. However, having too few layers can lead to underfitting, while an excessive number can cause overfitting.
• Number of Nodes (Units) per Layer: Known as the network's width, this parameter influences the learning capability. Finding the right balance is essential; too many nodes can lead to overfitting, while too few may result in underfitting.
• Type of Activation Function: Activation functions are used in hidden layers to introduce non-linearity. The choice of activation function for the output layer depends on the specific problem being addressed.

For more visual insights on neural network hyperparameters, check out the following video:

Hyperparameters Governing Training Processes

These hyperparameters play a vital role in controlling how the network is trained:

• Type of Optimizer: Optimizers are algorithms designed to minimize loss functions by adjusting network parameters. Popular choices include various forms of gradient descent.
• Learning Rate: This hyperparameter determines the size of the steps taken during optimization. A well-chosen learning rate is critical, as a value that is too high can destabilize training, while one that is too low can prolong convergence.
• Loss Function Type: The loss function is essential for measuring network performance during training. The choice of loss function depends on the type of problem being solved, whether it’s regression or classification.

To further enhance your understanding, watch this video summarizing neural network hyperparameters:

Regularization Hyperparameters

Hyperparameters that manage regularization directly address the issue of overfitting in neural networks. Common ones include:

• Regularization Parameter (λ): This factor controls the level of L1 and L2 regularization.
• Dropout Rate: This parameter defines the probability of dropping nodes during training to mitigate overfitting.

Conclusion

Hyperparameters are integral to the performance of machine learning and deep learning models. They not only define the architecture of the neural network but also influence the training process and regularization techniques. Understanding and optimizing these hyperparameters is essential for developing robust models.

For those keen on deepening their knowledge, consider exploring other related resources, including articles on parameters versus hyperparameters and best practices for choosing activation functions.

Thank you for reading! If you have any questions or feedback, feel free to reach out. Happy learning!

Rukshan Pramoditha

2022–09–16