Intro to Image Recognition with Deep Learning using Apache Spark and BigDL

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Alex Kalinin

The presentation given at the 2017 Data Science Camp conference, http://www.sfbayacm.org/data-science-camp-2017/.

Tecnologia

Image Recognition with
Deep Learning using Apache
Spark and BigDL
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Feed-forward network
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Fully Connected
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Fully Connected
Input Size:
Connections:
40,000
1,600,000,000
200
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10 layers: 16 billion
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Convolutional Neural Networks
• LeNet-5 network developed in 1998 by Yann LeCun
• Torsten Hubel and David Wiesel
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Hubel & Wiesel
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1363130/

Hierarchical Visual Cortex
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Hierarchical Visual Cortex
Lines,
Dots
Orientation,
Movement
High-Level
Shapes
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Local Receptive Fields
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1363130/

Convolutional Network
• Hierarchical processing
• Localized receptive fields
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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Convolution
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Convolution
Only four weights
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Convolution
0.10 -0.06
0.24 0.17
Filter
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Source: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow-ebook
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Source: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow-ebook
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Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Pooling
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/Source: https://cs231n.github.io/convolutional-networks/

Convolutional Network
• Hierarchical processing
• Localized receptive fields
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Convolution Pooling Convolution PoolingInput FC FC
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Convolutional Network
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Convolution Pooling Convolution PoolingInput FC FC
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/Source: https://www.clarifai.com/technology

Workshop
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

Questions?
•GitHub: https://github.com/alex-kalinin/lenet-bigdl
•LinkedIn: https://www.linkedin.com/in/alexkalinin/
Alex Kalinin
https://www.linkedin.com/in/alexkalinin/

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Intro to Image Recognition with Deep Learning using Apache Spark and BigDL

1. Image Recognition with Deep Learning using Apache Spark and BigDL Alex Kalinin https://www.linkedin.com/in/alexkalinin/

2. Feed-forward network Alex Kalinin https://www.linkedin.com/in/alexkalinin/

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6. -0.53 0.01 -0.17 0.70 0.51 ? ? 𝑦 = 𝑅𝑒𝐿𝑈( 𝑤𝑖 ∗ 𝑥𝑖 ) 43 37 45 40 -1.56 ? ? ? Alex Kalinin https://www.linkedin.com/in/alexkalinin/

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9. ? ? 𝑦 = 𝑅𝑒𝐿𝑈( 𝑤𝑖 ∗ 𝑥𝑖 ) 43 37 45 40 -.11 ? 0 11.9 Alex Kalinin https://www.linkedin.com/in/alexkalinin/

10. ? ? 𝑦 = 𝑅𝑒𝐿𝑈( 𝑤𝑖 ∗ 𝑥𝑖 ) 43 37 45 40 ? 0 11.9 0 Alex Kalinin https://www.linkedin.com/in/alexkalinin/

11. ? ? 𝑦 = 𝑅𝑒𝐿𝑈( 𝑤𝑖 ∗ 𝑥𝑖 ) 43 37 45 40 0.15 0 11.9 0 Alex Kalinin https://www.linkedin.com/in/alexkalinin/

12. -0.67 ? 𝑦 = 𝑅𝑒𝐿𝑈( 𝑤𝑖 ∗ 𝑥𝑖 ) 43 37 45 40 0.15 0 11.9 0 Alex Kalinin https://www.linkedin.com/in/alexkalinin/

13. 0 ? 𝑦 = 𝑅𝑒𝐿𝑈( 𝑤𝑖 ∗ 𝑥𝑖 ) 43 37 45 40 0.15 0 11.9 0 Alex Kalinin https://www.linkedin.com/in/alexkalinin/

14. 0 0.52 𝑦 = 𝑅𝑒𝐿𝑈( 𝑤𝑖 ∗ 𝑥𝑖 ) 43 37 45 40 0.15 0 11.9 0 Alex Kalinin https://www.linkedin.com/in/alexkalinin/

15. Feed-forward network Alex Kalinin https://www.linkedin.com/in/alexkalinin/

16. Alex Kalinin https://www.linkedin.com/in/alexkalinin/

17. Alex Kalinin https://www.linkedin.com/in/alexkalinin/

18. Alex Kalinin https://www.linkedin.com/in/alexkalinin/

19. Fully Connected Alex Kalinin https://www.linkedin.com/in/alexkalinin/

20. Fully Connected Alex Kalinin https://www.linkedin.com/in/alexkalinin/

21. Fully Connected Alex Kalinin https://www.linkedin.com/in/alexkalinin/

22. Fully Connected Input Size: Connections: 40,000 1,600,000,000 200 200 200 200 10 layers: 16 billion Alex Kalinin https://www.linkedin.com/in/alexkalinin/

23. Convolutional Neural Networks • LeNet-5 network developed in 1998 by Yann LeCun • Torsten Hubel and David Wiesel Alex Kalinin https://www.linkedin.com/in/alexkalinin/

24. Hubel & Wiesel Alex Kalinin https://www.linkedin.com/in/alexkalinin/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1363130/

25. Alex Kalinin https://www.linkedin.com/in/alexkalinin/

26. Alex Kalinin https://www.linkedin.com/in/alexkalinin/

27. Hierarchical Visual Cortex Alex Kalinin https://www.linkedin.com/in/alexkalinin/

28. Hierarchical Visual Cortex Lines, Dots Orientation, Movement High-Level Shapes Alex Kalinin https://www.linkedin.com/in/alexkalinin/

29. Local Receptive Fields Alex Kalinin https://www.linkedin.com/in/alexkalinin/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1363130/

30. Convolutional Network • Hierarchical processing • Localized receptive fields 0 0 0 0 0 1 0 0 0 0 0 1 2 3 4 5 6 7 8 9 Alex Kalinin https://www.linkedin.com/in/alexkalinin/

31. Fully Connected Alex Kalinin https://www.linkedin.com/in/alexkalinin/

32. Convolution Alex Kalinin https://www.linkedin.com/in/alexkalinin/

33. Convolution Alex Kalinin https://www.linkedin.com/in/alexkalinin/

34. Convolution Alex Kalinin https://www.linkedin.com/in/alexkalinin/

35. Convolution Alex Kalinin https://www.linkedin.com/in/alexkalinin/

36. Convolution Only four weights Alex Kalinin https://www.linkedin.com/in/alexkalinin/

37. Convolution 0.10 -0.06 0.24 0.17 Filter Alex Kalinin https://www.linkedin.com/in/alexkalinin/

38. Source: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow-ebook Alex Kalinin https://www.linkedin.com/in/alexkalinin/

39. Source: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow-ebook 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 Alex Kalinin https://www.linkedin.com/in/alexkalinin/

40. Pooling Alex Kalinin https://www.linkedin.com/in/alexkalinin/Source: https://cs231n.github.io/convolutional-networks/

41. Convolutional Network • Hierarchical processing • Localized receptive fields 0 0 0 0 0 1 0 0 0 0 0 1 2 3 4 5 6 7 8 9 Convolution Pooling Convolution PoolingInput FC FC Alex Kalinin https://www.linkedin.com/in/alexkalinin/

42. Convolutional Network 0 0 0 0 1 0 0 0 0 0 0 1 2 3 4 5 6 7 8 9 Convolution Pooling Convolution PoolingInput FC FC Alex Kalinin https://www.linkedin.com/in/alexkalinin/Source: https://www.clarifai.com/technology

43. Workshop Alex Kalinin https://www.linkedin.com/in/alexkalinin/

44. Questions? •GitHub: https://github.com/alex-kalinin/lenet-bigdl •LinkedIn: https://www.linkedin.com/in/alexkalinin/ Alex Kalinin https://www.linkedin.com/in/alexkalinin/

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Intro to Image Recognition with Deep Learning using Apache Spark and BigDL

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Intro to Image Recognition with Deep Learning using Apache Spark and BigDL

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