The Building Blocks of Neural Networks- Mat Kelcey (By ThoughtWorks)
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This document outlines the basic building blocks of neural networks. It discusses dense connections, embeddings for text processing, tied weights to reduce parameters, recurrence for sequences, and convolutions for images. These building blocks can be combined to solve complex problems, like predicting house prices from house features, predicting the next word in a sequence of text, image classification to detect outdoor scenes, and image captioning that combines convolutional and recurrent neural networks.
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The Building Blocks of Neural Networks- Mat Kelcey (By ThoughtWorks)
- 1. THE BUILDING BLOCKS OF NEURAL NETWORKS Mat Kelcey
- 3. PROBLEM: PREDICTING HOUSE PRICES size (m2) price 190 ?
- 4. 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 ?
- 5. PROBLEM: PREDICTING HOUSE PRICES
- 6. PROBLEM: PREDICTING HOUSE PRICES
- 7. PROBLEM: PREDICTING HOUSE PRICES
- 8. A SIMPLE MODEL price = m x size
- 9. A SIMPLE MODEL price = m x size something we know
- 10. A SIMPLE MODEL price = m x size something we want to know
- 11. A SIMPLE MODEL price = m x size something we need to estimate
- 12. A GRAPHICAL REPRESENTATION size price m price = m x size
- 13. A GRAPHICAL REPRESENTATION price m1 m2 size distance to city price = m1 x size + m2 x distance
- 14. A GRAPHICAL REPRESENTATION house size distance block size age ... price
- 15. A GRAPHICAL REPRESENTATION house size distance block size age ... price
- 19. BUILDING BLOCKS 1. dense connections
- 20. WHAT ABOUT DIFFERENT TYPES OF DATA block size 62 m2 distance to city 11.0 km block size 250 m2
- 21. 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."
- 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. WHAT ABOUT TEXT? "To be, or not to be."
- 24. PROBLEM: PREDICTING THE NEXT WORD input: output: To be or ?
- 25. REPRESENTING TEXT {be, not, or, to} "To be, or not to be."
- 26. REPRESENTING TEXT {be, not, or, to} "To be, or not to be." {b, e, n, o, r, t}
- 27. REPRESENTING A WORD be not or to
- 28. REPRESENTING A WORD 1 0 0 0 be not or to
- 29. REPRESENTING A WORD 0 0 1 0 be not or to
- 30. REPRESENTING A WORD 1 0 0 0 be not or to -0.3 0.1 0.4
- 31. 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
- 32. BUILDING BLOCKS 1. dense connections 2. embeddings for processing text
- 33. 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
- 34. TIED WEIGHTS keep all these weights the same!!
- 35. PARTS OF A NETWORK input words represented as 0s & 1s
- 36. PARTS OF A NETWORK input words represented as 3 values
- 37. PARTS OF A NETWORK a combination of the three words that represents the phrase
- 38. PARTS OF A NETWORK the expected the next word
- 39. BUILDING BLOCKS 1. dense connections 2. embeddings for processing text 3. tied weights for reducing parameters
- 40. PREDICTING THE NEXT WORD: v0
- 41. PREDICTING THE NEXT WORD: v0
- 42. PREDICTING THE NEXT WORD: v1
- 43. PREDICTING THE NEXT WORD: v1
- 44. PREDICTING THE NEXT WORD: v1
- 45. PREDICTING THE NEXT WORD: v1
- 46. PREDICTING THE NEXT WORD: v1
- 47. PREDICTING THE NEXT WORD: v1
- 48. PREDICTING THE NEXT WORD: v1
- 49. PREDICTING THE NEXT WORD: v1 tied weights!
- 50. BUILDING BLOCKS 1. dense connections 2. embeddings for processing text 3. tied weights for reducing parameters 4. recurrence for handling sequences
- 51. BUT WHAT ABOUT IMAGES?
- 52. PROBLEM: "IS OUTDOOR" DETECTOR input: output: True
- 54. "IS OUTDOOR" DETECTOR 768 x 512 x 3 = 1,179,648 pixels 512 768 3
- 56. "IS OUTDOOR" DETECTOR number of parameters = 768 x 512 x 3 = 1,179,648
- 57. "IS OUTDOOR" DETECTOR number of parameters = 384 x 256 x 3 = 294,912
- 58. "IS OUTDOOR" DETECTOR number of parameters = 384 x 256 x 3 = 294,912
- 59. 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. PROBLEM: CAPTIONING IMAGES input: output: A group of young people playing a game of frisbee. Show and Tell: A Neural Image Caption Generator: Vinyals et al
- 68. THANK YOU For questions or suggestions: Mat Kelcey matkelcey@ThoughtWorks.com