Anthill Inside 2017
On theory and concepts in Machine Learning, Deep Learning and Artificial Intelligence. Formerly Deep Learning Conf.
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu
28 Fri
29 Sat 09:00 AM – 05:40 PM IST
30 Sun
##About AnthillInside:
In 2016, The Fifth Elephant branched into a separate conference on Deep Learning. Anthill Inside is the new avataar of the Deep Learning conference.
Anthill Inside attempts to bridge the gap bringing theoretical advances closer to functioning reality.
Proposals are invited for full length talks, crisp talks and poster/demo sessions in the area of ML+DL. The talks need to focus on the techniques used, and may be presented independent of the domain wherein they are applied.
We also invite talks on novel applications of ML+DL, and methods of realising the same in hardware/software.
Case studies of how DL and ML have been applied in different domains will continue to be discussed at The Fifth Elephant.
https://anthillinside.in/2017/
##Format:
Anthill Inside is a two-track conference:
We are inviting proposals for:
You must submit the following details along with your proposal, or within 10 days of submission:
##Selection Process:
We expect you to submit an outline of your proposed talk, either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal to start evaluating your proposal.
You can check back on this page for the status of your proposal. We will notify you if we either move your proposal to the next round or if we reject it. Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.
A speaker is NOT confirmed a slot unless we explicitly mention so in an email or over any other medium of communication.
There is only one speaker per session. Entry is free for selected speakers.
We might contact you to ask if you’d like to repost your content on the official conference blog.
##Travel Grants:
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.
##Commitment to Open Source:
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for 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), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.
##Important Dates:
##Contact:
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Please note, we will not evaluate proposals that do not have a slide deck and a video in them.
Hosted by
Shubham Dokania
@shubhamdokania
Submitted Jun 10, 2017
Deep Learning, although a trending topic, appears as a challenging topic to beginners. There has been significant improvement in Deep Learning frameworks in the recent years, making it easier for everyone to hop-on the Machine Learning bandwagon. This workshop is aimed at giving participants a hands-on experience of a variety of deep learning techniques, while discussing about the underlying mathematical concepts involved.
While no prior deep learning background is assumed, participants are required to review a few mathematical concepts. Jupyter notebooks will be provided for the participants to tweak and run on their own laptops using VirtualBox.
This workshop shall be beneficial for those who seek to journey through the interesting applications of Deep Learning and get a kickstart into the field. Participants already familiar with Deep Learning will get to build exciting applications.
The workshop will start from the very basics of Neural Networks, building an intuitive thinking of how and why these techniques work, and quickly progress to getting hands-on using open source frameworks (Keras, Tensorflow) including the training of a deep network on simple problems to get ‘warmed up’.
This will be followed by more detailed discussions about large scale models, where participants will use several pre-trained models for the demonstration of applications such Knowledge transfer with deep neural networks. The applications to be discussed in depth include:
Jupyter notebooks with examples will be provided to participants for experimenting and gain a deep understanding of the techniques and the not-so-scary mathematics behind this all.
Participants will need a laptop with VirtualBox installed (if not running directly on your platform), with the following libraries installed:
Participants should have knowledge of basic Python scripting (functions, classes etc.), along with an understanding of some matrix mathematics, introductory calculus and statistics (Variances, Probability distributions etc.).
Shubham Dokania is currently a Machine Learning Instructor at Coding Blocks, while parallely working as a research intern at IIIT Delhi, supervised by Dr. Ganesh Bagler. Completed his B.Tech in Mathematics and Computing from Delhi Technological University (Formerly DCE). At Coding Blocks, he teaches undergrad/grad students and industry professionals about the techniques of Machine Learning with an inclination towards the research background of the methods discussed. Shubham has also spoken at local meetups including PyData Delhi Meetup, DTU workshop sessions and Bootcamps across New Delhi.
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