From Neural Networks to Deep Learning

Yoav Kantor


Artificial intelligence has become the hottest talk of the 21st century, bringing us self- driving cars, speech recognition and early disease diagnosis. Deep Learning specifically, has become one of the most popular buzz-words in the field. Deep Learning capabilities are utilized to solve numerous problems in various domains. In this talk, we will look under the hood of Deep Learning, starting with introduction to Neural Networks (Deep learning basic block) and reviewing different network types. Then we will delve into Deep Learning by exploring common implementations such as Convolutional Neural Networks and Deep Auto Encoders. The implantations will be followed by examples of dimensionality reduction, anomaly detection and image recognition. we will understand the math behind deep learning and the secrets to its success.
*Basic understanding of statistics and machine learning is required.


Photo of Yoav Kantor

Yoav Kantor finished his M.Sc. degree at the Technion in 2013 and works at IBM Research - Haifa lab since.

He is part of the Debating Technologies team, that develops a Machine- Learning based system that given a controversial topic can automatically generate relevant persuasive arguments by scanning massive text corpora. He will be happy to share his experience and insights gained via implementing an automatic large scale labeling mechanism, which is used on a daily basis by the project team.

Requested 1 time

Lecture languages



AI / Automation

Duration options

1 hour

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In-countryOutside of country: Open for discussionRemote via video conference



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