The Conference for Machine Learning Innovation

Machine Learning: The Bare Math Behind Libraries

Session
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Register until October 20:
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✓ Team discount
✓ Extra Specials for Freelancers
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Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos

Machine learning is one of the hottest buzzwords in technology today as well as one of the most innovative fields in computer science – yet people use libraries as black boxes without basic knowledge of the field. In this session, we will strip them to bare math, so next time you use a machine learning library, you’ll have a deeper understanding of what lies underneath.
During this session, we will first provide a short history of machine learning and an overview of two basic teaching techniques: supervised and unsupervised learning. We will start by defining what machine learning is and equip you with an intuition of how it works. We will then explain gradient descent algorithm with the use of simple linear regression to give you an even deeper understanding of this learning method.
Then we will project it to supervised neural networks training. Within unsupervised learning, you will become familiar with Hebb’s learning and learning with concurrency (winner takes all and winner takes most algorithms).
We will use Octave for examples in this session; however, you can use your favorite technology to implement presented ideas. Our aim is to show the mathematical basics of neural networks for those who want to start using machine learning in their day-to-day work or use it already but find it difficult to understand the underlying processes.
After viewing our presentation, you should find it easier to select parameters for your networks and feel more confident in your selection of network type, as well as be encouraged to dive into more complex and powerful deep learning methods.

This Session originates from the archive of Diese Session stammt aus dem Archiv von BerlinBerlin, MunichMunich and  und MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Singapore Singapore .

This Session originates from the archive of Diese Session stammt aus dem Archiv von BerlinBerlin, MunichMunich and  und MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Berlin Berlin .

This Session originates from the archive of Diese Session stammt aus dem Archiv von BerlinBerlin, MunichMunich and  und MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Munich Munich .

This Session Diese Session originates from the archive of stammt aus dem Archiv von BerlinBerlin, MunichMunich and  und MunichMunich . Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

Behind the Tracks