The document provides an overview of key concepts in deep learning including deep neural networks, forward propagation, activation functions, cost functions, regularization, gradient descent, optimization, and backpropagation. It notes that backpropagation uses the chain rule of calculus to calculate the gradient of the loss function with respect to network parameters.

1. Building blocks of
Deep Learning
Keshan Sodimana

2. Today’s menu
● What is a deep neural network
● How forward propagation works
● What are activation, cost function and regularization
● Gradient descent and optimizations
● Backpropagation
● Let’s practice….

3. BUT WAIT...
Do I need to have a good
knowledge in Mathematics? I
have done nothing on Maths
since my ALs.

4. y = mx + c
z = f(x)
∑f(x)
dx/dy
Do you remember?

5. What is a deep neural network

6. Forward propagation

7. Activation functions

8. Regularization

9. Cost function
Our goal is to minimize the cost.

10. Gradient descent
Finding the global minimum

11. Optimization

12. Backpropagation
Spoiler Alert : This could be the hardest part to digest

13. Backpropagation
Remember : back
propagation is used to
calculate the gradient
of the loss function with
respect to the
parameters

14. Can you remember the chain rule in calculus?

15. Ok let’s go for a test run...