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.
- 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:
- Draft slides, mind map or a textual description detailing the structure and content of your talk.
- 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.
- 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.
- Proposals will be filtered and shortlisted by an Editorial Panel.
- Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
- 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.
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.
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”.
- 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 firstname.lastname@example.org or call +91-7676332020.
Please note, we will not evaluate proposals that do not have a slide deck and a video in them.
Deep Learning Applications: A hands-on approach
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:
- Image Classification problems
- Text Processing with word vectors (GloVe)
- Transfer Learning with pre-trained models (Knowledge Transfer)
- Deep dream and Artistic style transfer (The algorithm powering Prisma)
- Reinforcement Learning with DQN (for simple games).
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:
- Tensorflow or Theano
- Numpy and Scipy stack
- OpenCV (optional, to use your camera for live demo of models)
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.