The Conference for Machine Learning Innovation

Machine Learning: The Bare Math Behind Libraries

Session
Join the ML Revolution!
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✓ML Intro Day for free
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Join the ML Revolution!
Register until August 12:
✓ML Intro Day for free
✓Save up to $380
Register Now
Join the ML Revolution!
Register until August 19:
✓ML Intro Day for free
✓Save up to €490
Register Now
Join the ML Revolution!
Register until August 19:
✓ML Intro Day for free
✓Save up to €490
Register Now
Join the ML Revolution!
Register until the conference starts:
✓ 2-in-1 conference special
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Register until the conference starts:
✓ 2-in-1 conference special
✓ 10 % Team Discount
Register Now
Infos
Wednesday, June 23 2021
15:15 - 16:00

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 belongs to the Diese Session gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Singapore Singapore .

This Session belongs to the Diese Session gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Berlin Berlin .

Take me to the full program of Zum vollständigen Programm von Munich Munich .

This Session Diese Session belongs to the gehört zum Programm von MunichMunich program. Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

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