Talk by Boris Yakubchik given at Creative Coding NYC Meetup on July 25th, 2018

1. Demystifying
Machine Learning
and Starting Today
Boris Yakubchik (Forbes)
July 25, 2018
Creative Coding NYC

2. Outline
1. About ML
2. Many examples
3. Looking inside a
neural network
4. Image recognition
5. Code examples

3. “Any sufficiently advanced technology is
indistinguishable from magic.”
Arthur C. Clarke

4. Machine Learning
≈
large set of mathematical approaches
to creating models
that can predict
outputs based on inputs
non-exhaustive
rough outline

5. Topic today:
Supervised Deep Learning
First step:
examples

6. Style Transfer

7. style2paints - PaintsTransfer.com

8. YOLO – object detection

9. Object Segmentation

10. DensePose

11. Image to sentence

12. Mario Klingemann

13. A.I. Waifu - make.girls.moe

14. Face Generation

15. DeepFake

17. Gradient descent
Optimizer
Loss
Activation
Overfitting
Back propagation
Epoch
VOCABULARY
AHEAD
Gradient descent
Optimizer
Loss
Layers
Gradient descent
Optimizer
Loss
Activation
Overfitting
Back propagation
Epoch
Layers
Tensors
Gradient descent

18. Dense Neural Network

19. To train a model you need labeled data
(input and output)

21. You set the hyperparameters

22. mycode.py

23. Tell your computer to train (learn)

24. YOU ARE DONE!
(and in seconds your model is trained)

25. But how does it work?

26. Here’s what your computer did:

27. 1. Set up all layers and neurons

28. 2. Fill neurons with random weights

29. 3. Put in first input
8
9

30. 4. Compute the result
8
9
(multiply by random weights)

31. 5. Make prediction
8
9

32. 6. Compute loss
8
9

33. 7. Back propagation
8
9

34. 8. Repeat for a full batch

35. 9. Update model weights

36. 10. Repeat until full epoch

37. 11. Repeat whole process until end condition

38. playground.tensorflow.org

39. Image recognition
with
Convolutional Neural Networks

40. Convolution

41. ConvNet

42. ConvNet

43. ConvNet

44. ConvNet
http://scs.ryerson.ca/~aharley/vis/conv/flat.html

45. How to get started today

46. ≈

47. Deep Learning with Python
François Chollet

48. Setting up your environment
1. Install python 3.6+
2. Install pip (Pip Installs Python)
3. Install venv (virtual environment)
4. alias py="python3"
5. alias activate="source venv/bin/activate"
• cd my-1337-ml-dir
• python3 -m venv venv
• source venv/bin/activate
• venv/Scripts/activate.bat if on PC
• pip install -r requirements.txt

49. keras
tensorflow
# get that GPU speed!
tensorflow-gpu
numpy==1.13.0
# tools
jupyter
# html5lib newer version needed to get tensorboard to work
html5lib==1.0.1
# data management
pandas
# utils
pylint
autopep8
matplotlib
# required for PIL -- image manipulation sometimes
pillow
opencv-python
requirements.txt

50. Jupyter Notebook / Jupyter Lab

51. What to do with it?

52. • Develop ML with JavaScript
• Run Existing Models
• Retrain Existing Models

53. One codebase = Win, Mac, & Linux apps

55. CODE