Fine-Tuning the Artificial Brain: A Quick Guide to Strategically Tune Neural Network Hyperparameters

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Neural networks have several hyperparameters that influence their architecture, training process, and performance. Here’s a comprehensive list of hyperparameters for neural networks:

1. Number of Hidden Layers

• n_layers: Number of hidden layers in the neural network.
• Influences the network’s capacity to model complex relationships within data.
• Start with a small number of layers and gradually increase complexity.

2. Number of Neurons in Each Hidden Layer

• n_neurons: Number of neurons in each hidden layer.
• Determines the expressive power and dimensionality of the hidden representations.
• Too few neurons may limit the model’s capacity, while too many may lead to overfitting.

3. Activation Function

• activation: Activation function used in each layer (e.g., 'relu', 'sigmoid', 'tanh').
• Shapes the non-linearity of the neural network, impacting its ability to learn complex patterns.

4. Weight Initialization