Derivatives Of Activation Functions (C1W3L08)  Summary and Q&A
TL;DR
This content discusses different activation functions used in neural networks and how to compute their derivatives.
Key Insights
 ๐๏ธ Activation functions play a crucial role in neural networks as they introduce nonlinearity and enable modeling complex relationships.
 ๐งก The sigmoid activation function has a range between 0 and 1, and its derivative can be simplified to G(Z) * (1  G(Z)).
 ๐งก The hyperbolic tangent activation function has a range between 1 and 1, and its derivative is calculated as 1  (G(Z) * G(Z)).
 ๐ค The Leaky ReLU activation function is defined as 0 for Z < 0 and Z for Z >= 0, with a derivative of 0 for Z < 0 and 1 for Z > 0.
Transcript
when you implement backpropagation for your neural network you need to really compute the slope or the derivative of the activation functions so let's take a look at our choices of activation functions and how you can compute the slope of these functions can see familiar sigmoid activation function and so for any given value of Z maybe this value ... Read More
Questions & Answers
Q: What is the purpose of activation functions in neural networks?
Activation functions introduce nonlinearity to the neural network, allowing it to learn complex patterns and make predictions. They determine the output of a neuron or node.
Q: How do you compute the derivative of the sigmoid activation function?
The derivative of the sigmoid function is calculated as G(Z) * (1  G(Z)), where G(Z) is the output of the sigmoid function.
Q: What is the derivative of the hyperbolic tangent activation function?
The derivative of the hyperbolic tangent function is computed as 1  (G(Z) * G(Z)), where G(Z) is the output of the hyperbolic tangent function.
Q: How is the derivative of the Leaky ReLU activation function defined?
The derivative of the Leaky ReLU function is 0 for Z < 0 and 1 for Z > 0. When Z = 0, the derivative is undefined, but it is commonly set to either 0 or 1 in practice.
Summary & Key Takeaways

The content provides an overview of activation functions and their derivatives, focusing on the sigmoid, hyperbolic tangent, Leaky ReLU, and ReLU functions.

It explains how to compute the derivatives of each activation function and provides examples to demonstrate their behavior.

The content concludes by mentioning the importance of computing the derivatives for implementing gradient descent in neural networks.