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- 1. Introduction to Machine Learning Nandita Naik 1
- 2. Agenda for Session 1 2 1. What can Machine Learning do in art? 2. How does ML work? 3. Main Concepts: a. How does a Computer Learn From Data? b. What is an ML Model? c. How Does Training and
- 3. What happens when art interacts with technology? 3
- 4. First Example of Technology for Art (1826 AD) 4
- 5. 5 Technology in Art Today: 2017 Go PhotoShop!
- 6. This is not Machine Intelligence, because a human is doing the thinking. 6
- 7. So What is Machine-Generated Art ? 7
- 8. 8 “The Red List” by Georgia O’Keeffe
- 9. 9
- 10. 10
- 11. 11
- 12. (source) 12
- 13. What Else Can Machine Intelligence do? 13
- 14. 14 Image recognition
- 15. Image captioning 15
- 16. This is just in the area of image processing. 16 ● Language Translation ● Music Generation ● Movie Recommendation in Netflix ● Call of Duty enemies ● Fraud detection ● Voice Recognition ● Disease detection
- 17. To get a closer look at ML, let’s investigate... 17
- 18. To get a closer look at ML, let’s investigate... SNAPCHAT! 18
- 19. 19
- 20. What’s going on? 20
- 21. Find the different parts of the person’s face! mesh or “point mask” 21
- 22. The mesh uses computer vision, which involves understanding images. 22 ● Snapchat’s algorithm learns from thousands of face pictures manually tagged with these dots, ● Then it predicts where the nose is in every new face that uses the puppy filter! Learning and Prediction : Key Concepts in Machine Learning
- 23. Machine Learning (ML) == learning from data 23
- 24. Machine learning works like our brain. 24 Just like us, it learns by observing, predicting and self-correcting.
- 25. Machine learning works like our brain. 25 Just like us, it learns by observing, predicting and self-correcting. What does that mean? Let’s take an example.
- 26. Suppose... The weekend’s coming up, and you just heard of a concert. You have to make a decision: should you go to the concert or stay home? 26
- 27. Should I go to the concert? 1. Do I have a test coming up? a. Which class? 2. What is the weather going to be like? 3. Do I have a friend that wants to come? a. Who? 4. How much do I like the band? 27
- 28. Should I go to the concert? 1. Do I have a test coming up? a. Which class? 2. What is the weather going to be like? 3. Do I have a friend that wants to come? a. Who? 4. How much do I like the band? But these factors aren’t all equal! Some may be more important or have a higher weight. 28
- 29. test? Nice weather? friend? test weight Weather weight friend weight Go or Stay? 29
- 30. Computers don’t know how important these factors are (what weights to give them.) At least, not right away. Problem 30
- 31. Solution They can learn the weights by studying lots and lots of examples. 31
- 32. To understand how a machine learns from data, we’ll take a simple example: linear regression. 32
- 33. 33 Grade Homework hours per week
- 34. 34 Grade Homework hours per week
- 35. 35 Grade Homework hours per week f(grade) = homework hours
- 36. 36 Grade Homework hours per week f(grade) = homework hours
- 37. Goal: Guess f(x)! Let’s assume f(x) is linear, so we can write... 37
- 38. Goal: Guess f(x)! Let’s assume f(x) is linear, so we can write... 38 y = wx + b
- 39. We don’t know what w and b are. Let’s guess! 39
- 40. 40Grade Homework hours per week We don’t know what w and b are. Let’s guess!
- 41. 41Grade Homework hours per week We don’t know what w and b are. Let’s guess!
- 42. 42Grade Homework hours per week We don’t know what w and b are. Let’s guess!
- 43. How good is our guess? 43
- 44. How good is our guess? Let’s measure loss. Loss = penalty for a wrong prediction “Less loss” is better than “more loss” 44
- 45. Goal: Minimize loss. 45
- 46. Goal: Minimize loss. 46 SGD (stochastic gradient descent)
- 47. gradient descent 47
- 48. Recap - linear regression: fitting a line to data - We randomly choose w and b (in y = wx + b) - Figure out our loss (“less loss” means we are closer) - Adjust our weights until you arrive at the 48
- 49. Questions? 49
- 50. 50 We know how to fit a line to our homework hours vs. grades data.
- 51. 51 We know how to fit a line to our homework hours vs. grades data. Not everything in life can be explained with a linear function!
- 52. 52 We know how to fit a line to our homework hours vs. grades data. Not everything in life can be explained with a linear function! How do we do better?
- 53. 53 Grade Homework hours per week
- 54. 54 Grade Homework hours per week
- 55. 55 Grade Homework hours per week
- 56. 56 Grade Homework hours per week
- 57. 57 overfitting: when f(x) doesn’t generalize
- 58. So we don’t want that kind of nonlinear function. 58
- 59. 59
- 60. In general, if we want a machine to learn a non- linear function, we use a neural network. 60
- 61. 61 w b y = wx + b
- 62. ReLU. f(x) = max(0,x) An activation function lets our network learn a non-linear function.
- 63. input weight output 63 computation max(0,x) x Activation functions
- 64. Now let’s talk what an actual neural network looks like. 64
- 65. A neural network is made up of many neurons, organized into layers. 65
- 66. neurons make up layers input data (x) output (y) input layer output layer The arrows are the weights and the inputs. 66
- 67. Often, in between the input and output layer... input data (x) output (y) input layer output layer The arrows are the weights and the inputs. 67
- 68. We have hidden layers. 68
- 69. We have hidden layers. 69 Job: transform inputs into something the output layer can use
- 70. 70 input data (x) output (y) input layer output layerhidden layer
- 71. A neural network... ● Processes millions of any kind of data ● Learns the properties of that data ● Generalizes those properties to data it’s never seen before 71
- 72. Recap 72 overfitting: when our function fits the data too closely and can’t generalize activation functions: let us to model non-linear functions A neural network is made up of many neurons, organized into layers. There are input, output, and hidden layers.
- 73. Questions? 73
- 74. For questions, contact me at nanni.naik(at)gmail.com or Github @nnaik39. 74