addressalign-toparrow-leftarrow-rightbackbellblockcalendarcameraccwcheckchevron-downchevron-leftchevron-rightchevron-small-downchevron-small-leftchevron-small-rightchevron-small-upchevron-upcircle-with-checkcircle-with-crosscircle-with-pluscrossdots-three-verticaleditemptyheartexporteye-with-lineeyefacebookfolderfullheartglobegmailgooglegroupshelp-with-circleimageimagesinstagramFill 1linklocation-pinm-swarmSearchmailmessagesminusmoremuplabelShape 3 + Rectangle 1ShapeoutlookpersonJoin Group on CardStartprice-ribbonShapeShapeShapeShapeImported LayersImported LayersImported Layersshieldstartickettrashtriangle-downtriangle-uptwitteruserwarningyahoo

Introduction to Deep Learning

t's been a long time dream to make the machine a more human like, giving it an Artificial Intelligence.

Teach it how to drive a car. Teach it how to identify objects in a given image for example give the computer an image captured in the front of a car and ask him if he can see pedestrians?
Give the computer a web page and ask him what the page is talking about? Give the computer a set of metrics taken from patient blood readings and ask him to provide a diagnostic about the patient health?

This is only a fraction of examples for machine learning applications there are many more fields such as speech recognition, recommendation systems, ranking and personalization of content all are often the basis of data products.

There are many ways these days, to teach a machine – give it some data, ask it to learn the pattern and then apply this pattern over a new unseen data.

Deep Learning is a new technique that outperformed many of the state-of-the-art algorithms in several research fields such as Audio, Text, and Vision.

Artifacts from a decade of hand crafted human research turned out to be inferior to this new technique in these research fields.

Deep Learning can be seen as The Rebirth of Neural Networks and as a Neural Network “fan” for many years I’m very excited to have the opportunity to see this rebirth. In this session I’ll be happy to share with you my passion to this field.

I’ll give an introduction to machine learning via Neural Networks, I’ll explain what features are, what auto encoders are and what Deep Learning is all about and give you a taste from machine learning new frontier.

Speaker: Al Yaros

Join or login to comment.

  • Shlomo Y.

    Was a nice talk.
    Always fun to visit Outbrain.

    March 20, 2014

  • Carmel K.

    Was really great!

    March 20, 2014

  • Itamar B.

    I am data analytics solutions product management expert.
    Looking for next challenge.
    [masked]

    March 19, 2014

  • Al Y.

    It will be available in Youtube based on the Reversim Talk. (It's currently pending at the Reversim Team).

    March 18, 2014

  • Gil M.

    Since the demand is so high, is there a chance that the lecture could be videotaped and uploaded for the benefit of those who can't make it or are on the waiting list?

    1 · March 18, 2014

  • Al Y.

    1. Repeat - Currently not planned to.

    2. "Math" Vs "Fluffy" - It contains just enough math to introduce the concepts. You can browse to http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial to get the idea and discover if it’s Math enough for you.

    1 · March 18, 2014

  • Matan S.

    Is it going to be mathematical or more towards the fluffy side? :)

    March 18, 2014

  • Marina B.

    Can't join tomorrow, is it planned to repeat?

    March 18, 2014

  • Al Y.

    Hebrew. It's the same as the Reversim talk.

    March 16, 2014

  • Justin A.

    english or hebrew?

    March 11, 2014

  • Dan

    Is it the same lecture that has been given in reversim?

    March 11, 2014

People in this
Meetup are also in:

Sign up

Meetup members, Log in

By clicking "Sign up" or "Sign up using Facebook", you confirm that you accept our Terms of Service & Privacy Policy