The document introduces deep learning and provides examples of its applications. It discusses using a neural network to recognize handwritten digits, how cost functions are used to evaluate accuracy, and how gradient descent helps optimize the network. Potential uses of deep learning mentioned include generating text, images, music, code and designs, and personalizing experiences based on user behavior and expressions. The document also lists several datasets and playgrounds for experimenting with deep learning, as well as resources for using it in frontend development.

1. Intro to Deep Learning
Ruxandra Burtica
ruxandra.burtica@gmail.com

3. What does learning look like?

5. Recognize handwritten digits (MNIST)

11. XW = Z
X W Z

14. Cost function
• Small when the network classifies correctly the digit
• Large otherwise
Compute average cost for all training examples ==> that is
our network’s cost

15. Gradient descent

17. How it applies to web

18. Generate
• Text
• Pictures
• Music
• Code
• Designs

19. Generate
• Text
• Pictures
• Music
• Code
• Designs

20. Generate
• Text
• Pictures
• Music
• Code
• Designs

21. Generate
• Text
• Pictures
• Music
• Code
• Designs

22. Generate

23. Personalize
• Based on user
behavior (previous
interactions)
• Based on user
expression (real-time )

24. Personalize
• Based on user
behavior (previous
interactions)
• Based on user
expression (real-time )

25. Personalize
• Based on user
behavior (previous
interactions)
• Based on user
expression (real-time )

26. Datasets

27. kaggle.com/datasets

28. aws.amazon.com/datasets

29. google.com/publicdata

30. Playgrounds

31. playground.tensorflow.org

32. selfdrivingcars.mit.edu/deeptraffic

33. deeplearnjs.org

34. Resources

35. • https://deeplearnjs.org
• https://transcranial.github.io/keras-js
• http://ml4a.github.io/demos/forward_pass_mnist
• https://blog.floydhub.com/turning-design-mockups-into-
code-with-deep-learning
• https://blog.insightdatascience.com/automated-front-
end-development-using-deep-learning-3169dd086e82
• https://github.com/Morphl-Project