Jul 2016
27 Mon
28 Tue
29 Wed
30 Thu
1 Fri 08:45 AM – 06:15 PM IST
2 Sat 08:15 AM – 02:15 PM IST
3 Sun
Deep Learning is a new area of research that is getting us closer in achieving one of the primary objectives of Machine Learning – Artificial Intelligence.
It is used widely in the fields of Image Recognition, Natural Language Processing (NLP) and Video Classification.
Deep Learning Conf is a single day conference followed by workshops on the second day. The conference will have full, crisp and lightning talks from morning to evening. The workshops on the next day will introduce participants to neural networks followed by two tracks of three-hour workshops on NLP and Computer Vision / AI. Participants can join either one of the two workshop tracks.
##Tracks
We are looking for talks and workshops from academics and practitioners of Deep Learning on the following topics:
We are inviting proposals for:
Proposals will be filtered and shortlisted by an Editorial Panel. Along with your proposal, you must share the following details:
If your proposal involves speaking about a library / tool / software that you intend to open source in future, the proposal will be considered only when the library / tool / software in question is made open source.
We will notify you about the status of your proposal within two-three weeks of submission.
Selected speakers have to participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you prepare for speaking at the conference.
There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), please consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
##Venue
CMR Institute of Technology, Bangalore
##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Hosted by
Utkarsh Sinha
@liquidmetal
Submitted May 26, 2016
Supervised and unsupervised learning techniques are relatively well understood in deep learning - Reinforcement learning is a new kind of learning that uses experience and interaction with the environment to make sense of the world around it.
An example of reinforcement learning is playing Atari video games. Creating a labeled dataset of “good moves” in the game is tedious and subjective. Unsupervised learning techniques might work but they don’t take advantage of the fact that the learning algorithm can interact with the game. Reinforcement learning combines the best of both techniques - no labels required and it can interact with the game world. This technique has also been used on the recent AlphaGo project by DeepMind.
Utkarsh Sinha a computer vision student at Carnegie Mellon University and currently at Microsoft Research. He’s been working in computer vision for the past few years and has been working in deep learning for the past year. His current research involves finding fine structures in images using deep learning techniques.
In the past, he has worked at DreamWorks Animation as a Technical Director and guided artists in modeling, texturing and lighting.
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