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MAT KELCEY Machine Learning Principal @mat_kelcey #EvolutionTW #ThoughtWorks
THE BUILDING BLOCKS OF NEURAL NETWORKS Mat Kelcey @mat_kelcey
BUILDING BLOCK #1
PROBLEM: PREDICTING HOUSE PRICES size (m2) price 190 ?
PROBLEM: PREDICTING HOUSE PRICES size (m2) price 160 $650,000 140 $560,000 200 $820,000 170 $660,000 180 $630,000 210 $800...
PROBLEM: PREDICTING HOUSE PRICES
PROBLEM: PREDICTING HOUSE PRICES
PROBLEM: PREDICTING HOUSE PRICES
A SIMPLE MODEL price = m x size
A SIMPLE MODEL price = m x size something we know
A SIMPLE MODEL price = m x size something we want to know
A SIMPLE MODEL price = m x size something we need to estimate
A GRAPHICAL REPRESENTATION size price m price = m x size
A GRAPHICAL REPRESENTATION price m1 m2 size distance to city price = m1 x size + m2 x distance
A GRAPHICAL REPRESENTATION house size distance block size age ... price
A GRAPHICAL REPRESENTATION house size distance block size age ... price
A GRAPHICAL REPRESENTATION
A GRAPHICAL REPRESENTATION
A GRAPHICAL REPRESENTATION
BUILDING BLOCKS 1. dense connections
WHAT ABOUT DIFFERENT TYPES OF DATA block size 62 m2 distance to city 11.0 km block size 250 m2
WHAT ABOUT DIFFERENT TYPES OF DATA block size 62 m2 distance to city 11.0 km block size 250 m2 "To be, or not to be."
WHAT ABOUT DIFFERENT TYPES OF DATA block size 62 m2 distance to city 11.0 km block size 250 m2 "To be, or not to be."
WHAT ABOUT TEXT? "To be, or not to be."
PROBLEM: PREDICTING THE NEXT WORD input: output: To be or ?
REPRESENTING TEXT {be, not, or, to} "To be, or not to be."
REPRESENTING TEXT {be, not, or, to} "To be, or not to be." {b, e, n, o, r, t}
REPRESENTING A WORD be not or to
REPRESENTING A WORD 1 0 0 0 be not or to
REPRESENTING A WORD 0 0 1 0 be not or to
REPRESENTING A WORD 1 0 0 0 be not or to -0.3 0.1 0.4
REPRESENTING A WORD 0 0 1 0 be not or to 0.1 0.5 -0.9 1 0 0 0 be not or to -0.3 0.1 0.4
BUILDING BLOCKS 1. dense connections 2. embeddings for processing text
PREDICTING THE NEXT WORD 0 0 0 1 be not or to be not or to 0 1 0 0 1 0 0 0 be not or to 0 0 1 0 be not or to
TIED WEIGHTS keep all these weights the same!!
PARTS OF A NETWORK input words represented as 0s & 1s
PARTS OF A NETWORK input words represented as 3 values
PARTS OF A NETWORK a combination of the three words that represents the phrase
PARTS OF A NETWORK the expected the next word
BUILDING BLOCKS 1. dense connections 2. embeddings for processing text 3. tied weights for reducing parameters
PREDICTING THE NEXT WORD: v0
PREDICTING THE NEXT WORD: v0
PREDICTING THE NEXT WORD: v1
PREDICTING THE NEXT WORD: v1
PREDICTING THE NEXT WORD: v1
PREDICTING THE NEXT WORD: v1
PREDICTING THE NEXT WORD: v1
PREDICTING THE NEXT WORD: v1
PREDICTING THE NEXT WORD: v1
PREDICTING THE NEXT WORD: v1 tied weights!
BUILDING BLOCKS 1. dense connections 2. embeddings for processing text 3. tied weights for reducing parameters 4. recurren...
BUT WHAT ABOUT IMAGES?
PROBLEM: "IS OUTDOOR" DETECTOR input: output: True
"IS OUTDOOR" DETECTOR
"IS OUTDOOR" DETECTOR 768 x 512 x 3 = 1,179,648 pixels 512 768 3
"IS OUTDOOR" DETECTOR
"IS OUTDOOR" DETECTOR number of parameters = 768 x 512 x 3 = 1,179,648
"IS OUTDOOR" DETECTOR number of parameters = 384 x 256 x 3 = 294,912
"IS OUTDOOR" DETECTOR number of parameters = 384 x 256 x 3 = 294,912
BUILDING BLOCKS 1. dense connections 2. embeddings for processing text 3. tied weights for reducing parameters 4. recurren...
BUILDING WITH BLOCKS
PROBLEM: CAPTIONING IMAGES Show and Tell: A Neural Image Caption Generator: Vinyals et al A group of young people playing ...
PROBLEM: CAPTIONING IMAGES
PROBLEM: CAPTIONING IMAGES conv net!
PROBLEM: CAPTIONING IMAGES embeddings! embeddings!
PROBLEM: CAPTIONING IMAGES
PROBLEM: CAPTIONING IMAGES recurrence!!
PROBLEM: CAPTIONING IMAGES
THANK YOU For questions or suggestions: Mat Kelcey matkelcey@ThoughtWorks.com
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Mat Kelcey- The Building Blocks of Neural Networks (Evolution)

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A key strength of neural networks is their composability which allows them to process different forms of input and output data. In this talk, Matt describes the basic building blocks of neural networks and how they handle varying types of data (such as images & text) as well as varying datasets sizes. He reviews how a novel set of problems can be solved with interesting combinations of these pieces.

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Mat Kelcey- The Building Blocks of Neural Networks (Evolution)

  1. 1. MAT KELCEY Machine Learning Principal @mat_kelcey #EvolutionTW #ThoughtWorks
  2. 2. THE BUILDING BLOCKS OF NEURAL NETWORKS Mat Kelcey @mat_kelcey
  3. 3. BUILDING BLOCK #1
  4. 4. PROBLEM: PREDICTING HOUSE PRICES size (m2) price 190 ?
  5. 5. PROBLEM: PREDICTING HOUSE PRICES size (m2) price 160 $650,000 140 $560,000 200 $820,000 170 $660,000 180 $630,000 210 $800,000 190 ?
  6. 6. PROBLEM: PREDICTING HOUSE PRICES
  7. 7. PROBLEM: PREDICTING HOUSE PRICES
  8. 8. PROBLEM: PREDICTING HOUSE PRICES
  9. 9. A SIMPLE MODEL price = m x size
  10. 10. A SIMPLE MODEL price = m x size something we know
  11. 11. A SIMPLE MODEL price = m x size something we want to know
  12. 12. A SIMPLE MODEL price = m x size something we need to estimate
  13. 13. A GRAPHICAL REPRESENTATION size price m price = m x size
  14. 14. A GRAPHICAL REPRESENTATION price m1 m2 size distance to city price = m1 x size + m2 x distance
  15. 15. A GRAPHICAL REPRESENTATION house size distance block size age ... price
  16. 16. A GRAPHICAL REPRESENTATION house size distance block size age ... price
  17. 17. A GRAPHICAL REPRESENTATION
  18. 18. A GRAPHICAL REPRESENTATION
  19. 19. A GRAPHICAL REPRESENTATION
  20. 20. BUILDING BLOCKS 1. dense connections
  21. 21. WHAT ABOUT DIFFERENT TYPES OF DATA block size 62 m2 distance to city 11.0 km block size 250 m2
  22. 22. WHAT ABOUT DIFFERENT TYPES OF DATA block size 62 m2 distance to city 11.0 km block size 250 m2 "To be, or not to be."
  23. 23. WHAT ABOUT DIFFERENT TYPES OF DATA block size 62 m2 distance to city 11.0 km block size 250 m2 "To be, or not to be."
  24. 24. WHAT ABOUT TEXT? "To be, or not to be."
  25. 25. PROBLEM: PREDICTING THE NEXT WORD input: output: To be or ?
  26. 26. REPRESENTING TEXT {be, not, or, to} "To be, or not to be."
  27. 27. REPRESENTING TEXT {be, not, or, to} "To be, or not to be." {b, e, n, o, r, t}
  28. 28. REPRESENTING A WORD be not or to
  29. 29. REPRESENTING A WORD 1 0 0 0 be not or to
  30. 30. REPRESENTING A WORD 0 0 1 0 be not or to
  31. 31. REPRESENTING A WORD 1 0 0 0 be not or to -0.3 0.1 0.4
  32. 32. REPRESENTING A WORD 0 0 1 0 be not or to 0.1 0.5 -0.9 1 0 0 0 be not or to -0.3 0.1 0.4
  33. 33. BUILDING BLOCKS 1. dense connections 2. embeddings for processing text
  34. 34. PREDICTING THE NEXT WORD 0 0 0 1 be not or to be not or to 0 1 0 0 1 0 0 0 be not or to 0 0 1 0 be not or to
  35. 35. TIED WEIGHTS keep all these weights the same!!
  36. 36. PARTS OF A NETWORK input words represented as 0s & 1s
  37. 37. PARTS OF A NETWORK input words represented as 3 values
  38. 38. PARTS OF A NETWORK a combination of the three words that represents the phrase
  39. 39. PARTS OF A NETWORK the expected the next word
  40. 40. BUILDING BLOCKS 1. dense connections 2. embeddings for processing text 3. tied weights for reducing parameters
  41. 41. PREDICTING THE NEXT WORD: v0
  42. 42. PREDICTING THE NEXT WORD: v0
  43. 43. PREDICTING THE NEXT WORD: v1
  44. 44. PREDICTING THE NEXT WORD: v1
  45. 45. PREDICTING THE NEXT WORD: v1
  46. 46. PREDICTING THE NEXT WORD: v1
  47. 47. PREDICTING THE NEXT WORD: v1
  48. 48. PREDICTING THE NEXT WORD: v1
  49. 49. PREDICTING THE NEXT WORD: v1
  50. 50. PREDICTING THE NEXT WORD: v1 tied weights!
  51. 51. BUILDING BLOCKS 1. dense connections 2. embeddings for processing text 3. tied weights for reducing parameters 4. recurrence for handling sequences
  52. 52. BUT WHAT ABOUT IMAGES?
  53. 53. PROBLEM: "IS OUTDOOR" DETECTOR input: output: True
  54. 54. "IS OUTDOOR" DETECTOR
  55. 55. "IS OUTDOOR" DETECTOR 768 x 512 x 3 = 1,179,648 pixels 512 768 3
  56. 56. "IS OUTDOOR" DETECTOR
  57. 57. "IS OUTDOOR" DETECTOR number of parameters = 768 x 512 x 3 = 1,179,648
  58. 58. "IS OUTDOOR" DETECTOR number of parameters = 384 x 256 x 3 = 294,912
  59. 59. "IS OUTDOOR" DETECTOR number of parameters = 384 x 256 x 3 = 294,912
  60. 60. BUILDING BLOCKS 1. dense connections 2. embeddings for processing text 3. tied weights for reducing parameters 4. recurrence for handling sequences 5. convolutions for processing images
  61. 61. BUILDING WITH BLOCKS
  62. 62. PROBLEM: CAPTIONING IMAGES Show and Tell: A Neural Image Caption Generator: Vinyals et al A group of young people playing a game of frisbee. output:input:
  63. 63. PROBLEM: CAPTIONING IMAGES
  64. 64. PROBLEM: CAPTIONING IMAGES conv net!
  65. 65. PROBLEM: CAPTIONING IMAGES embeddings! embeddings!
  66. 66. PROBLEM: CAPTIONING IMAGES
  67. 67. PROBLEM: CAPTIONING IMAGES recurrence!!
  68. 68. PROBLEM: CAPTIONING IMAGES
  69. 69. THANK YOU For questions or suggestions: Mat Kelcey matkelcey@ThoughtWorks.com

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