What are the best practices for training neural networks?

What are the best practices for training neural networks?

Some best practices include data normalization, using appropriate architectures, and regularization techniques.

Can you explain why data normalization is important?

Data normalization helps in speeding up the training process and improving the model's performance by ensuring that all input features contribute equally.

What architectures are commonly used for neural networks?

Common architectures include feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), each suited for different tasks.

How does regularization help in training?

Regularization techniques like dropout and L2 regularization help prevent overfitting by adding a penalty for complexity in the model.

What are some common pitfalls to avoid during training?

Common pitfalls include overfitting, underfitting, not using enough data, and failing to tune hyperparameters properly.