Cutting edge of Machine Learning

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Cutting edge of Machine Learning

  1. 1. Machine Learning The Cutting Edge Sergii Shelpuk Director, Data Science SoftServe, Inc. sshel@softserveinc.com
  2. 2. Classification Problem Recognize what is a bike and what is a moon
  3. 3. Classification Problem Classifier ©A. Ng
  4. 4. Classification Problem pixel intensity
  5. 5. Classification Problem Raw data does not represent the picture well. You need some smart features contains wheels contains seas
  6. 6. Feature Extraction Classifier Featureextractor ©A. Ng
  7. 7. Feature Extraction Can we do better?
  8. 8. Neural Networks a a a a a a a a a a a a a a features bike moon
  9. 9. Neural Networks
  10. 10. Neural Networks aX a0 a1 a2 w0 w1 w2 Activation function: aX = f(a0, a1, a2, w0, w1, w2) Example (logistic): aX = 1 / (1 + e-(a0*w0+a1*w1+a2*w2))
  11. 11. Autoencoder
  12. 12. Autoencoder © H. Lee et al.
  13. 13. Autoencoder © Q Le et al.
  14. 14. Deep Learning Neural Network Pre-trained as Autoencoder Typical classification neural network Moon
  15. 15. Deep Learning Neural NetworkVideoText/NLPImages ©A. Ng
  16. 16. Deep Learning Neural Network Hints and Tips  Using unlabeled data  Avoiding overfitting  Computational efficiency
  17. 17. Using Unlabeled Data wheels handlebar
  18. 18. Avoiding Overfitting Sparsity constraint limits variance of autoencoder
  19. 19. Avoiding Overfitting Dropout ensures generalization of the neural network
  20. 20. Computational Efficiency  Thousands of cores  Base Clock: 300-900 MHz  Memory: 2-6 Gb  Performance: up to 3.5 Tflops  Instruction-level parallelism  Shared memory  Up to 4 devices in cluster GPU computing provides cheapest computational power
  21. 21. Feature Learning: MNIST Data: Features:
  22. 22. Feature Learning: Galaxy Zoo Data: Features:
  23. 23. Thank you!

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