The chain rule is a fundamental concept in Calculus used to differentiate
The chain rule finds extensive application in the fields of deep learning, machine learning, and Artificial Intelligence. It is commonly used to handle regression problems and helps in the process of backward propagation in neural networks.
You can see here for more details of other differentiation rules.
The chain rule states that the derivative of a composite function
Let's apply the chain rule to find the derivative of a composite function.
Question: Consider the function
Answer: To use the chain rule, we treat
Find the derivative of the outer function
Next, find the derivative of the inner function
Now, apply the chain rule formula:
So, the derivative of
Question: Consider the function
Answer: To use the chain rule, we treat
Find the derivative of the outer function
Next, find the derivative of the inner function
Now, apply the chain rule formula:
So, the derivative of
Now that you know the chain rule in differentiation, you can challenge yourself with a quiz.
Chain rule in differentiation
Differentiation the function
The chain rule holds significance in calculus and diverse mathematical domains for multiple reasons. Here are a few of its applications:
Calculating rate of change: The chain rule is essential for determining how a change in one variable affects the overall function. It helps us understand the rate of change of the composite function with respect to the underlying variables.
Backpropagation in machine learning: In deep learning and neural networks, the chain rule plays a crucial role in backpropagation, which is a key algorithm for training the models. It helps compute the gradients needed to update the model's parameters during the learning process.
Handling complex functions: The chain rule allows us to break down complex functions into their constituent parts and calculate their derivatives separately.
The chain rule is a powerful tool in Calculus, allowing us to differentiate composite functions efficiently. Its application simplifies complex problems, providing a fundamental framework for understanding rates of change in interconnected systems.
Free Resources