# Description This is not a straightforward problem, and there is no unique or optimal solution. When increasing the number of neurons in the hidden layers, the following effects are typically observed: 1. **Increased Predictive Power and Risk of Overfitting:** Adding more neurons can improve the model's ability to capture complex patterns in the data, but it also increases the risk of overfitting. 2. **Decreased Probability of Getting Stuck in a Local Minimum:** A larger number of neurons can provide more flexibility in the network, potentially reducing the likelihood of the optimization process getting trapped in local minima. 3. **Increased Training Time:** A larger network with more neurons typically requires more time to train due to the increased computational complexity. In practice, it is common to set the number of [[Neuron]] in the hidden layers to be between one to two times the number of input neurons in the network. --- ## References - [[Deep learning - Anna Bosch Rué Jordi Casas Roma Toni Lozano Bagén]]