Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Upcoming SlideShare
×

# Oleksandr Honchar "Deep learning for signal processing"

113 views

Published on

Data Science Practice

Published in: Engineering
• Full Name
Comment goes here.

Are you sure you want to Yes No
Your message goes here
• Be the first to comment

• Be the first to like this

### Oleksandr Honchar "Deep learning for signal processing"

1. 1. DEEP LEARNING for signal processing (or time series) Oleksandr Honchar Mawi Solutions
2. 2. • AI solutions architect @ Mawi Solutions • Dottore magistrale in mathematics candidate @ UNIVR • Calling myself AI expert in Linkedin
3. 3. SIGNALS IN THE WILD 🦁
4. 4. SIGNALS IN THE WILD 🦁
5. 5. SIGNALS IN THE WILD 🦁
6. 6. MAWI BAND ♥️
7. 7. CLASSICAL APPROACHES 🔬 • Classification - time domain analysis
8. 8. CLASSICAL APPROACHES 🔬 • Classification - time domain analysis - frequency domain analysis
9. 9. CLASSICAL APPROACHES 🔬 • Classification - time domain analysis - frequency domain analysis - nearest neighbors
10. 10. CLASSICAL APPROACHES 🔬 • Classification - time domain analysis - frequency domain analysis - nearest neighbors - ML
11. 11. CLASSICAL APPROACHES 🔬 • Classification - time domain analysis - frequency domain analysis - nearest neighbors - ML • Regression - ARIMA models
12. 12. CLASSICAL APPROACHES 🔬 • Classification - time domain analysis - frequency domain analysis - nearest neighbors - ML • Regression - ARMA models - smoothing / decomposition
13. 13. CLASSICAL APPROACHES 🔬 • Classification - time domain analysis - frequency domain analysis - nearest neighbors - ML • Regression - ARMA models - smoothing / decomposition - nonlinear dynamics
14. 14. CLASSICAL APPROACHES 🔬 • Classification - time domain analysis - frequency domain analysis - nearest neighbors - ML • Regression - ARMA models - smoothing / decomposition - nonlinear dynamics - ML
15. 15. DEEP LEARNING 🦁 TCE conference, 2014
16. 16. DEEP LEARNING 🦁 • RNN
17. 17. DEEP LEARNING 🦁 • RNN
18. 18. DEEP LEARNING 🦁 • RNN
19. 19. DEEP LEARNING 🦁 • RNN 1.Theoretical infinite memory 2.Multistep prediction ability 3.Truncated implementation 4.Cant train in parallel 5.Difficult to optimize 6.Slow in inference 7.Doubtful superior performance!!!
20. 20. DEEP LEARNING 🦁 • RNN • CNN
21. 21. DEEP LEARNING 🦁 • RNN • CNN
22. 22. DEEP LEARNING 🦁 • RNN • CNN • RNN + CNN
23. 23. DEEP LEARNING 🦁 • RNN • CNN • RNN + CNN • Autoregressive CNN
24. 24. DEEP LEARNING 🦁 • RNN • CNN • RNN + CNN • Transformer
25. 25. MAWI BAND ♥️
26. 26. Other successes 🦁 Sales forecasting Wikipedia traffic
27. 27. DEEP LEARNING 🦁 • RNN • CNN • RNN + CNN • Autoregressive CNN • Other tasks
28. 28. DEEP LEARNING 🦁 • RNN • CNN • RNN + CNN • Autoregressive CNN • Other tasks
29. 29. HYBRID SOLUTIONS 🐙
30. 30. HYBRID SOLUTIONS 🐙
31. 31. TAKEAWAYS 📚 • Signals are everywhere • Autoregressive CNN > CNN > RNN • Cluster in embedding space • Use GANs not just to generate • Combine DL and classics if you can • It works for NLP, speech and other sequences as well!
32. 32. TCE conference, 2014
33. 33. Home reading 1. When Recurrent Models Don't Need To Be Recurrent 2. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling 3. DEEP TEMPORAL CLUSTERING: FULLY UNSUPERVISED LEARNING OF TIME-DOMAIN FEATURES 4. REAL-VALUED (MEDICAL) TIME SERIES GENERATION WITH RECURRENT CONDITIONAL GANS 5. Time-series Extreme Event Forecasting with Neural Networks at Uber FB: @rachnogstyle MEDIUM: @alexrachnog