Machine Learning

Hyperparameters in an Artificial Neural Network (ANN)

In this article, I will explain Hyperparameters in an Artificial Neural Network (ANN).

Basically, hyperparameters are user-defined parameters that are not learned by the neural network during training. Instead, they are set by the user prior to training. In fact, hyperparameters control various aspects of the training process, including the architecture of the network, the optimization algorithm used to train the network, and the training process itself.

The following list shows some common hyperparameters in an artificial neural network (ANN).

Common Hyperparameters in an Artificial Neural Network

  1. Learning rate. This is a parameter that controls the step size of the optimization algorithm used to update the network’s weights during training. Actually, a higher learning rate can lead to faster convergence. However, it may cause the optimization process to be unstable. Whereas a lower learning rate may be slower but more stable.
  2. The number of hidden layers. Basically, this is a hyperparameter that determines the number of layers of neurons between the input and output layers in the network. In general, the number of hidden layers can impact the complexity of the model and its ability to learn more complex patterns in the data.
  3. Number of neurons per hidden layer. This hyperparameter controls the number of neurons in each hidden layer of the network. In fact, a larger number of neurons can lead to more complex models. Also, it may increase the risk of overfitting.
  4. Activation function. Further, this hyperparameter determines the type of nonlinearity used in the neurons of the network. So, common choices include the sigmoid, ReLU, and tanh functions.
  5. Regularization. Basically, it is a technique used to prevent overfitting in the network. The common methods include L1 and L2 regularization, dropout, and early stopping.
  6. Batch size. Another hyperparameter that determines the number of samples used in each iteration of the training process is the batch size. Actually, a larger batch size can lead to more stable convergence, but may also require more memory.
  7. The number of epochs. It determines the number of times the network sees the entire training data during the training process. A higher number of epochs can lead to better performance, but may also increase the risk of overfitting.

Further Reading

Python Practice Exercise

Examples of OpenCV Library in Python

Examples of Tuples in Python

Python List Practice Exercise

A Brief Introduction of Pandas Library in Python

A Brief Tutorial on NumPy in Python

Unleashing Creativity and Innovation with Drone Competitions in College

Breaking Boundaries: Innovative Project Ideas for Drones with Machine Learning


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