What is neural network architecture?
An example of an AI chatbot conversation about neural network architecture.
What is neural network architecture?
Neural network architecture refers to the structure of a neural network, including the number of layers, types of layers, and how they are connected.
Can you explain the different types of layers used in neural networks?
Sure! Common types of layers include input layers, hidden layers, and output layers. Each serves a specific purpose in processing data.
What is the role of activation functions in these layers?
Activation functions introduce non-linearity into the network, allowing it to learn complex patterns. Examples include ReLU, sigmoid, and tanh.
How do I choose the right architecture for my problem?
Choosing the right architecture depends on the problem type, data size, and desired outcomes. Experimentation is key!
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