Anthill Inside 2017

On theory and concepts in Machine Learning, Deep Learning and Artificial Intelligence. Formerly Deep Learning Conf.

##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.

Topics: we are looking for talks covering the following:

  • Machine Learning with end-to-end application
  • Deep Learning
  • Artificial Intelligence
  • Hardware / software implementations of advanced Machine Learning and Deep Learning
  • IoT and Deep Learning
  • Operations research and Machine Learning

Anthill Inside is a two-track conference:

  • Talks in the main auditorium and hall 2.
  • Birds of Feather (BOF) sessions in expo area.

We are inviting proposals for:

  • Full-length 40-minute talks.
  • Crisp 15-minute how-to talks or introduction to a new technology.
  • Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
  • Hands-on workshop sessions of 3 and 6 hour duration where participants follow instructors on their laptops.
  • Birds of Feather (BOF) sessions.

You must submit the following details along with your proposal, or within 10 days of submission:

  1. Draft slides, mind map or a textual description detailing the structure and content of your talk.
  2. Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant or last year at Deep Learning.
  3. If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.

##Selection Process:

  1. Proposals will be filtered and shortlisted by an Editorial Panel.
  2. Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
  3. Proposers are also encouraged to vote and comment on other proposals submitted here.

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.

Selection Process Flowchart

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:

  • Deadline for submitting proposals: July 10
  • First draft of the coference schedule: July 15
  • Tutorial and workshop announcements: June 30
  • Final conference schedule: July 20
  • Conference date: July 30


For more information about speaking proposals, tickets and sponsorships, contact 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

Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more

Satwik Kansal


Developing agents with Deep Reinforcement learning

Submitted Jun 20, 2017

Most of the people would have heard of Deepmind’s AI getiing really good at playing Atari games.

In this talk, we’ll focus on developing a similar but simpler agent that uses Reinforcement Learning algorithms to learn to play such games on its own. The session will progress from a gentle introduction to the Reinforcement Learning (RL) concepts, steps involved gradually in identifying and modelling a RL problem. The complexity of RL algorithm described in the session will escalate from simple table based Q-learning algorithm, to simple neural network based RL and then finally to highly efficient Deep Q networks.

For demonstration, we’ll be using Open AI gym to simulate the game environment and ipython notebooks to document and display the code.

After this beginner-friendly session, the audience will be able to understand how Reinforcement Learning algorithms work at a broader level and develop their own game agent.


First 10 minutes:

Initially, the audience is introduced to Reinforcement Learning (RL) and some of the standard terms and concepts like Agents, state, policy, etc.

Next 10 minutes:

We will walk through the implementation of Q-Learning (an RL technique) to develop an Agent that learns to adapt to the game environment provided by Open AI and gets smarter with every move by learning ‘policies’ and ‘strategies.’

Next 15 minutes:

We’ll talk through the design of simple single-layered Neural network algorithm and finally, go through the implementation of Deep-Q network for the same game environment.

Final 10 minutes:

The session will conclude with some general discussion regarding scope and limitations of these Reinforcement learning techniques and the current state of the Reinforcement Learning in terms of applications and availability of open source libraries.


Basic programming knowledge, rough knowledge of how Neural Networks work will also be helpful.

Speaker bio

A Junior pursuing Bachelors in Software Engineering. Also, a Freelance Software Developer having experience in Machine Learning and Web Development.

I actively contribute to some Open Source projects. In my leisure time, I like to learn about new technologies like AI, Decentralized Applications, AR/VR, explore the trends, and work on ideas involving these technologies.

I usually conduct sessions on Python and Android development as a part of DTU: Open Source Software Development group. I’ve gained all the knowledge this talk requires from some quality MOOCs, popular articles on the web, and Udacity’s Machine Learning Nanodegree that I was enrolled in.

I regularly participate in Hackathons and community meetups and have won several hackathons over the past year. And lastly, I like all things Python!



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Hosted by

Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more