The document provides an overview of deep learning, including: - An introduction to deep learning concepts like perceptrons, neural networks, forward and back propagation, and activation functions. - How deep learning can be applied to problems in computer vision, text processing, audio, and unstructured data. - The importance of regularization techniques like dropout and batch normalization to prevent overfitting in neural networks. - That deep learning requires large amounts of data and compute power to be effective.

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Basics of Deep Learning
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2. Agenda
● Introduction to deep learning
● Understanding Perceptron
● Forward Propagation
● Activation Functions
● Building Neural Networks with Perceptrons
● Deep Neural Networks
● Applying Neural Network
● Training Neural Network
● Back Propagation
● Gradient Descent
● Overfitting

3. What is deep learning ?

4. Deep Learning Success
● Computer Vision
● Text Processing
● Audio
● Unstructured data
● In many places can replace humans

5. Why Now ?

6. When not to use Deep Learning ?
● More data is required for Deep Learning
● More Compute Power
● Models less interpretable
“Don’t kill a mosquito with a cannon ball”
Don’t use Deep Learning if you don’t need to

7. Perceptron
Foundational Building Block of Deep Learning

8. Understanding Perceptron

9. Activation Functions
The purpose of activation functions is to introduce non-linearities into the network

10. Perceptron Example

11. Purpose of Activation Functions
The purpose of activation functions is to introduce non-linearities into the network

12. Building Neural Networks
From Perceptrons

13. Representation

14. Representation - With Hidden Layer

15. Understanding Loss

16. Cost Function

17. Cost Function for Binary Classification

18. Cost Function for Regression

19. Gradient Descent Algorithm

20. Backpropagation - 1

21. Backpropagation - 2

22. Overfitting
Problems with complex models

23. Underfit - Perfectfit - Overfit

24. Regularization - Dropout

25. Regularization - Batch Normalization
● To increase the stability of a neural network, batch normalization
normalizes the output of a previous activation layer by subtracting the
batch mean and dividing by the batch standard deviation.

26. Regularization - Early Stopping

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