What are the key components of deep learning architectures?

What are the key components of deep learning architectures?

Deep learning architectures typically consist of layers, neurons, activation functions, and optimization algorithms.

Can you explain what activation functions do?

Activation functions introduce non-linearity into the model, allowing it to learn complex patterns.

What are some common types of activation functions?

Common activation functions include ReLU, Sigmoid, and Tanh, each with its own advantages and use cases.

How do optimization algorithms affect training?

Optimization algorithms adjust the weights of the network to minimize the loss function, impacting convergence speed and accuracy.