What are the key components of deep learning architecture?
The key components include input layers, hidden layers, output layers, activation functions, and loss functions.
Can you explain the role of activation functions?
Activation functions introduce non-linearity into the model, allowing it to learn complex patterns.
What types of activation functions are commonly used?
Common activation functions include ReLU, Sigmoid, and Tanh, each with its own advantages.
How do I choose the right architecture for my problem?
Choosing the right architecture depends on the data type, problem complexity, and computational resources.
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