Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Properties of Activat

The video explains the importance of activation functions in neural networks, emphasizing their role in introducing nonlinearity, which enhances the network's representational power. It discusses practical choices for activation functions, highlighting the common use of a single function across a network, except for the output layer. The video details the sigmoid and ReLU functions, noting their properties and computational aspects. It also outlines the essential properties of activation functions, such as nonlinearity, ease of computation, and differentiability, and demonstrates their implementation in Torch.

Describe the ReLU activation function and its characteristics.

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What are the key properties that an activation function should have?

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Why is differentiability an important property for activation functions?

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