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 and enhancing representational power. It covers common activation functions like sigmoid and ReLU, discussing their properties and applications. The video also highlights the essential characteristics of activation functions, such as nonlinearity, ease of computation, and differentiability, which are crucial for computing gradients in neural networks. Finally, it demonstrates how to use these functions in Torch.

Why are activation functions crucial in neural networks?

• They help in collapsing neurons into a single function.

• They are used only for output neurons.

• They introduce nonlinearity, enhancing the network's representational power.

• They make the network linear and easy to compute.

What is the common practice regarding activation functions in neural networks?

• Changing activation functions dynamically during training.

• Using a different activation function for each neuron.

• Using no activation functions at all.

• Applying a single activation function throughout the network.

Which activation function is known for its simplicity and efficiency?

• ReLU

• Tanh

• Sigmoid

• Softmax

What is a key property of the Sigmoid activation function?

• It is linear for all input values.

• It outputs values between 0 and 1.

• It outputs values between -1 and 1.

• It is non-differentiable.

What is a crucial property of activation functions for learning in neural networks?

• They must be non-differentiable.

• They should be linear.

• They need to be differentiable.

• They should be complex to compute.

Why is differentiability important for activation functions?

• It simplifies the network architecture.

• It allows for the computation of gradients during learning.

• It makes the function easier to compute.

• It ensures the function is nonlinear.

Which property is NOT essential for an activation function?

• Being non-differentiable

• Differentiability

• Ease of computation

• Nonlinearity