Deep Learning CNN Convolutional Neural Networks with Python - Activation Function

The video discusses the importance of activation functions in neural networks, focusing on their role in enabling nonlinearity and enhancing the network's decision-making capabilities. It covers various types of activation functions, including sigmoid and Relu, and explains their properties and applications. The video emphasizes the necessity of nonlinear functions to prevent the network from collapsing into a simple linear regression model. It concludes with a brief mention of the next topic, the training module.

Why are activation functions crucial in neural networks?

• They introduce non-linearity, allowing complex decision boundaries.

• They simplify the network architecture.

• They help in linearizing the output.

• They reduce the number of neurons required.

What is a characteristic of a sigmoid activation function?

• It outputs the input value directly.

• It outputs 1 for positive inputs and 0 for negative inputs.

• It outputs values between 0 and 1, depending on the input size.

• It outputs values between -1 and 1.

What happens if a neural network lacks non-linear activation functions?

• It requires fewer layers.

• It can solve more complex problems.

• It becomes more efficient.

• It becomes a simple linear regression model.

Which activation function is known for its efficiency in computation?

• ReLU

• Leaky ReLU

• Sigmoid

• Tanh

What is a key property of activation functions for effective training?

• They should be non-differentiable.

• They should be linear.

• They should be differentiable.

• They should be complex to compute.

Which function is typically used at the output layer for classification problems?

• Linear

• Tanh

• Sigmoid

• ReLU

What is the main advantage of using ReLU over other activation functions?

• It is computationally efficient and easy to implement.

• It is always linear.

• It is non-differentiable.

• It outputs negative values for negative inputs.