Machine learning accuracy
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Machine learning algorithms have limits and are better at interpolation than extrapolation. To improve accuracy, organizations are investing heavily in artificial intelligence and machine learning technologies to process vast amounts of data. Accuracy can be optimized by improving algorithms, data engineering, and obtaining more and higher quality data. Regularly refreshing models as new data is collected ensures the models continue to provide useful business insights. However, machine learning is only as useful as the overall business strategy, so the models and strategy must work together to maximize insights. The quest for accuracy through machine learning is an endless process of continuous improvement.
Machine learning accuracy
- 1. Machine Learning The Endless Quest for Accuracy Photo by Caden Crawford on Flickr
- 3. Machine Learning in a Few Words
- 4. Machine Learning Algorithms have limits, they’re good at Interpolation not Extrapolation … What does that mean? Photo by Ricardo Gomez Angel on Unsplash
- 5. Learning how to drive in a calm city doesn’t prepare you for … Photo by Derek Liang on Unsplash
- 6. … that’s the next level …
- 8. Forrester Predicts Investment In Artificial Intelligence Will Grow 300% in 2017 Accenture found that 85% of executives plan to invest extensively in AI-related technology over the next three years. Photo by Dan Grinwis on Unsplash Yet the hype is BIG! Why?
- 9. Photo by Patrick Lindenberg on Unspla The price of 1GB was 500,000$ in 1980 and 0,03$ in 2015
- 10. There exists a Huge Technology Ecosystem Hadoop … Spark … Photo by Christine Donaldson on Unspla
- 11. GPU are 8 to 10 times faster than CPU for Neural Networks Processing Photo by Maxime Rossignol on Unspla
- 12. Photo by Markus Spiske on Unspla We now can quickly process vast amounts of data This allows Reinforcement and Supervised learning to achieve incredible accomplishments
- 13. Reinforcement Learning: Learn by simulating your environnement
- 14. Supervised Learning: Learn thanks to tagged historical data
- 15. Data Engineering Raw Data Learning Data Test Data Algorithm Model Accuracy How does it work? Photo by Caroline Methot on Unspla To optimise this process: Improve the Algorithms!
- 16. Improve the Algorithm ex: MNIST 1998 : Linear Classifier -> 12% error rate 2012 : committee of 35 conv. Net -> 0.23%
- 17. 14 millions+ tagged Images the challenge is to recognize the objects 2010 : 25% error rate 2015 : 5% error rate
- 18. Data Engineering Raw Data Learning Data Test Data Algorithm Model Accuracy To optimise this process : Improve the Data Engineering! Photo by Patryk Grądys on Unspla
- 20. Feature Engineering 1506335498 Monday 25 September 2017 10:31:38 This is a date in Unix time It was a Monday Morning In September In 2017 Photo by Shane Rounce on Unspla
- 21. Photo by Annie Spratt on Unsplas
- 22. Data Engineering Raw Data Learning Data Test Data Algorithm Model Accuracy To optimise this process : Get More Data! Photo by Dennis Kummer on Unspla
- 23. “Information is the oil of the 21st century, and analytics is the combustion engine” Mr. Sondergaard from Gartner in 2011
- 24. More Data = More Information = Better Accuracy Demographic Behavioural Geographical Weather Photo by Ramón Salinero on Unspla Open Data
- 25. Behavior change quickly how do we adapt? Photo by Jeremy Perkins on Unspla Refresh your Models as fast as possible Collect your Data from every where Use this Data to make better decisions Collect the results to build better models Build a Virtuous Cycle
- 26. Machine Learning gives Great Insights but Machine Learning is only as good as your Business Strategy Photo by Samson Duborg-Rankin on Unspla
- 27. Example: If you Sell Products Online Photo by Wojtek Witkowski on Unsplas If you optimise your campain on the probability of click on your ads If you choose to optimise the probability of selling procuct You will naturaly build models that will avoid Bots Your Models will be honey pots for Bots Bots click but don’t buy
- 28. Photo by Johannes Plenio on Unspla Let’s Wrap-up You have Defeated the Algorithm Complexity Your Data are Cleaner than Ever and Full of Business Insights You have Gathered all the Data there is Your models are Refreshed as Fast as Possible Your Business Strategy Maximises your Model’s Insights and Vice Versa
- 29. Now you can … Do it again! Welcome to the Endless Quest for Accuracy. Photo by Paul Trienekens on Unspl
Editor's Notes
- #2: https://www.flickr.com/photos/cadencrawford/8296313952 Caden Crawford
- #3: This is the team of Fujitsu’s Center of Excellence in Artificial Intelligence, we are located inside Polytechnique’s Start-up Incubator I started to work 17 years ago … back in the days when Machine Learning was called Data-Mining I worked mostly in data related industries, KXEN and Criteo
- #4: Machine Learning is a concept dating back to the 50s, it’s a field of computer science that gives computers the ability to learn without being explicitly programmed, they are learning from experience to accomplish a certain task.
- #11: Those technologies run on commodity hardware, it’s cheaper and cheaper to store and process vaste amount of data
- #15: Fraud detection data
- #16: Let say that you want to predict the probability of an ad to be clicked on?
- #17: The MNIST database of handwritten digits, has a training set of 60,000 examples, and a test set of 10,000 examples.
- #18: The ImageNet project is a large visual database designed for use in visual object recognition software research. In 2012, a deep convolutional neural net achieved 16%; in the next couple of years, error rates fell to a few percent Today, many consider ImageNet solved—the error rate is incredibly low at around 2%. But that’s for classification, or identifying which object is in an image. This doesn’t mean an algorithm knows the properties of that object, where it comes from, what it’s used for, who made it, or how it interacts with its surroundings.
- #19: Let say that you want to predict the probability of an ad to clicked on?
- #21: Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data. The Unix epoch (or Unix time or POSIX time or Unix timestamp) is the number of seconds that have elapsed since January 1, 1970 (midnight UTC/GMT)
- #23: Let say that you want to predict the probability of an ad to clicked on?
- #25: 30% of businesses are Weather dependent