Machine Learning Wars: Deep Learning vs Gradient Boosting Machines
•
0 likes•231 views
This slides were presented in Bilisim IO Techtalks in 2018-12-09 (Istanbul)
Recommended
Recommended
More Related Content
Similar to Machine Learning Wars: Deep Learning vs Gradient Boosting Machines
Similar to Machine Learning Wars: Deep Learning vs Gradient Boosting Machines (20)
More from Sefik Ilkin Serengil
More from Sefik Ilkin Serengil (8)
Recently uploaded
Recently uploaded (20)
Machine Learning Wars: Deep Learning vs Gradient Boosting Machines
- 1. Machine Learning Wars: Deep Learning vs GBM Sefik Ilkin Serengil software developer @ softtech
- 2. sefiks.com serengil Sefik Ilkin Serengil Software Developer @ ‘10 CS graduate, MSc @ ‘11 Instructor @
- 6. We can recognize face identities with Deep Learning https://youtu.be/tSU_lNi0gQQ
- 7. Or we can find facial expressions and emotion https://youtu.be/Y7DfLvLKScs
- 8. We also know that what would it look like if Vincent van Gogh had painted Galatasaray University https://sefiks.com/2018/07/20/artistic-style-transfer-with-deep-learning/
- 9. https://sefiks.com/2017/12/10/transfer-learning-in-keras-using-inception-v3/ We have outcomes of Tech Giants in seconds through transfer learning
- 11. Microsoft developed its bot Tay.ai with deep learning
- 12. It becomes a racist one in hours
- 13. This is because we cannot respond why and how questions in deep learning as well…
- 14. Decisions in deep learning are basically made by matrix multiplication and this is not understandable by human
- 15. We might move the hammel with an alternative algorithm
- 16. If the decision is wearing rain jacket, we can read reasons backwardly
- 18. We check just a single feature in same level of tree
- 21. Decision tree consists of 700 lines of if statetements! Luckily, decision tree algorithm can exctract these rules and developers wouldn’t write these if statements.
- 25. Decision Trees are non-linear ML models
- 26. Classify this data set
- 27. Linear Model E.g. Perceptron, Linear SVM Decision Trees fails! They can handle
- 28. Now, classify this data set
- 29. Linear Model Decision Tree Simple linear model can easily classify this task Decision trees still can model the problem, but it wouldn’t be easy!
- 31. A single tree is not strong enough
- 32. Gradient Boosting Machines (GBM) Gradient Descent + Boosting Gradient Boosting Decision Trees (GBDT) Multiple Additive Regression Trees (MART) Gradient Boosting Regression Trees (GBRT)
- 34. 100 m 70 m
- 35. 30 m 15 m
- 36. 15 m 20 m
- 37. -5 m 5 m
- 41. Weak classifiers can come together and create a strong classifier
- 42. XGBoost: A Scalable Tree Boosting System https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf 2015 KAGGLE GRAND PRIX AMONG 29 WINNING SOLUTIONS Podium Ceremony 1 2 3 GBM 17 solutions Deep Neural Nets 11 solutions 9 solutions GBM + Neural Nets
- 43. GBM Frameworks
- 45. Adoption https://www.quora.com/Does-the-new-CatBoost-algorithm-by-Yandex-outperform-the-infamous-XGBoost-in-Kaggle-competitions LightGBM is 10 times faster than XGBoost!
- 46. https://github.com/szilard/GBM-perf LightGBM performs faster in CPU whereas XGBoost runs faster in GPU. Consider the hardware you have before adopting a framework
- 47. What data science methods are used at finance industry? https://www.kaggle.com/surveys/2017 Decision trees are more popular in data science world
- 48. My favorite machine learning is Neural Nets. That’s my favorite. My 2nd favorite machine learning is SVD. Everyone says, oh don’t you prefer gradient boosted trees? I know GBTs are great but I like NN best and I like SVD next best. Francois Chollet Creator of Keras, Google AI Researcher https://twitter.com/fchollet/status/992465859904876544
- 51. Thank you for your attention! sefiks.com serengil