Over Mt. Stupid to Deep Learning

My slides for my talk at Code.Talks 2018 about MAchine Learning, Deep Learning and my journey through this topics. Video of talk may come later.

Transcript

1. OVER MT. STUPID TO DEEP LEARNING… Carsten Sandtner \\ @casarock Photo by Archie Binamira from Pexels https://www.pexels.com/photo/man-wearing-white-shirt-brown-shorts-and-green-backpack-standing-on-hill-672358/
2. ABOUT://ME My name is Carsten and I’m Technical Director at mediaman GmbH in Mayence (Mainz). I’m a moz://a Techspeaker and I love the open web and everything about open standards for the web! I’m tweeting as @casarock 
 
 … and I asked myself: What is this AI thing?
4. „ “ –Amy Webb - SXSW 2018 The robots are going to come and kill us all but not before they take over all of our jobs
5. „ “ –Amy Webb - SXSW 2018 The artificial intelligence ecosystem —  flooded with capital, hungry for commercial applications, and yet polluted with widespread, misplaced optimism and fear — will continue to swell
6. „ “ –Google CEO Sundar Pichai - Google I/O 2017 From mobile first to AI first
7. Confidence Wisdom Mt. Stupid Valley of despair Slope of enlightenment Plateau of sustainability
8. Photo by Markus Spiske temporausch.com from Pexels https://www.pexels.com/photo/aerial-photography-of-white-mountains-987573/
9. BASICS!
11. SOME HISTORY!
12. HISTORY 1950: Neuronal Networks! ~1980: Machine Learning Today: Deep Learning Photo by Dick Thomas Johnson https://www.flickr.com/photos/31029865@N06/14810867549/
13. AI Machine Learning Deep Learning
14. MACHINE LEARNING
15. MACHINE LEARNING Statistics! Correlation Regression a => weights Const => Offset/Bias Y = Const + aX1 + bX2 + cX3 + ... + zXn
16. Activation Function Output Input Weight Offset z = ∑w0x0 w0 x0 +b y = f(z) y0
17. ACTIVATION FUNCTIONS 0 2 4 6 -4 -6 -2 0.0 0.2 0.4 0.6 0.8 1.0 0 2 4 6 -4 -6 -2 0 1 2 3 4 5 Sigmoid ReLu R(z) = max(0, z) sig(z) = 1/(1+e-z) Rectifier Linear Unit
18. EXAMPLE: PREDICTIVE MAINTENANCE
19. SIMPLE EXAMPLE Machine Sensors x1, x2 and x3
 Weights a, b and c Y = Const + ax1 + bx2 + cx3 Const: Value when x1, x2 and x3 are 0 Training data: 100.000 Datasets. Keep 25.000 for validation Train with 75.000 -> Vary weights until result (Y) is ok Verify your model with the 25.000 sets for validation
21. HOW MACHINES LEARN
22. HOW MACHINES LEARN Supervised Learning Unsupervised Learning Reinforcement Learning
23. SUPERVISED Useful for predictions and classifications Popular use case: 
 Image recognition Needs classified training sets.
24. UNSUPERVISED Useful for segmentation and clustering Clustered data needs revision by a human Good for dimensional reduction
25. REINFORCEMENT has not a defined result for training data. Using rewards for good results - if it isn’t good do it never again, bad boy! Example: Learn how to play a game just while analyse every pixel Popular Example: 
 Alpha Go
26. MY JOURNEY! Confidence Wisdom Mt. Stupid Valley of despair Slope of enlightenment Plateau of sustainability
27. Photo by Paula May on Unsplash https://unsplash.com/photos/AJqeO_-ifx0
28. DEEP LEARNING (DL)
29. NEURONAL NETWORKS
30. ME. Confidence Wisdom Mt. Stupid Valley of despair Slope of enlightenment Plateau of sustainability
31. Photo by Mario Álvarez on Unsplash https://unsplash.com/photos/M1YdS0g8SRA
32. NEURON Neuron Activation Function Output Input Weight Offset z = ∑wixi w0 x0 +b y = f(z) y0 x1 xn w1 wn y1 yn LAYER
33. . . . . Input Layer Output Layer Hidden Layers
34. . . . . Input Layer Output Layer Hidden Layers Forward pass Init with random weights and offsets
35. DETERMINE THE ERROR
36. ERROR FUNCTION f(x) = mx + b
37. ERROR FUNCTION f(x) = mx + b Mean Squared Error = Avg(Error2)
38. . . . . Input Layer Output Layer Hidden Layers Back propagation Propagate the error into every neuron err err err err err err err err err err err
39. BACKPROPAGATION For every neuron from output to input Check neurons share in the error (using weights) Fine tune weights (in very small steps eg. 0.001) This is a Hyperparameter: Learning Rate! Applies to the whole network Every weight adjusted? Start a new run This is called an new Epoch (next Hyperparameter!)
40. GRADIENT DESCENT
41. OUR GOAL: CONVERGENCE
42. CONVERGENCE Always take a look at your error. Is it minimal? Stop learning! Validate your model with other data (not the learning data!) Try to avoid overfitting!
43. ME. Confidence Wisdom Mt. Stupid Valley of despair Slope of enlightenment Plateau of sustainability
44. Photo by Will Langenberg on Unsplash https://unsplash.com/photos/S9uZOfeYi1U
45. CONVOLUTIONAL NEURAL NETWORKS
46. CONVOLUTIONS
47. CONVOLUTIONS
48. CONVOLUTIONS We train this little thing!
49. CONVOLUTIONAL NEURAL NETWORK Conv ReLu Pool Conv Conv Conv Conv Conv ReLu ReLu ReLu ReLu ReLu Pool Pool Dog Cat Mouse FC
50. Photo by Will Langenberg on Unsplash https://unsplash.com/photos/S9uZOfeYi1U
51. ME. Confidence Wisdom Mt. Stupid Valley of despair Slope of enlightenment Plateau of sustainability
52. Photo by Natalia_Kollegova https://pixabay.com/de/fata-morgana-das-gebirge-berge-2738131/
53. THOUGHT ABOUT MY GOALS Confidence Wisdom Mt. Stupid Valley of despair Slope of enlightenment Plateau of sustainability
54. WHAT IS IT SUITABLE FOR?
58. SOME BETTER EXAMPLES Categorization of products in an online shop using their images. Using cognitive services for Natural Language Processing (NLP) voice or text based Using a cloud based AI Service!
59. AZURE COGNITIVE SERVICES https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/
60. „ “ No matter what you do, you need sh*tloads of data!
61. EVERYTHING IS GREAT!
62. DEPENDS…
63. . . . . Input Layer Output Layer Hidden Layers
64. WHO IS RESPONSIBLE?
65. Photo by Katie Salerno https://www.pexels.com/photo/love-people-romance-engagement-18396/
66. Photo by Cade Roberts on Unsplash https://unsplash.com/photos/H0Aud5lhupc
67. Photo by Martin Shreder on Unsplash https://unsplash.com/photos/5Xwaj9gaR0g
68. AI (ML/DL) IS AWESOME. USE IT WISELY. Thank you! @casarock for