The Fifth Elephant is India’s most renowned data science conference. It is a space for discussing some of the most cutting edge developments in the fields of machine learning, data science and technology that powers data collection and analysis.
Machine Learning, Distributed and Parallel Computing, and High-performance Computing continue to be the themes for this year’s edition of Fifth Elephant.
We are now accepting submissions for our next edition which will take place in Bangalore 28-29 July 2016.
We are looking for application level and tool-centric talks and tutorials on the following topics:
- Deep Learning
- Text Mining
- Computer Vision
- Social Network Analysis
- Large-scale Machine Learning (ML)
- Internet of Things (IoT)
- Computational Biology
- ML in healthcare
- ML in education
- ML in energy and ecology
- ML in agriculrure
- Analytics for emerging markets
- ML in e-governance
- ML in smart cities
- ML in defense
The deadline for submitting proposals is 30th April 2016
This year’s edition spans two days of hands-on workshops and conference. We are inviting proposals for:
- Full-length 40 minute talks.
- Crisp 15-minute talks.
- Sponsored sessions, 15 minute duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on Workshop sessions, 3 and 6 hour duration.
Proposals will be filtered and shortlisted by an Editorial Panel. We urge you to add links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience. Blurbs or blog posts covering the relevance of a particular problem statement and how it is tackled will help the Editorial Panel better judge your proposals.
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.
We will notify you about the status of your proposal within three weeks of submission.
Selected speakers must participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for 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.
- Revised paper submission deadline: 17 June 2016
- Confirmed talks announcement (in batches): 13 June 2016
- Schedule announcement: 30 June 2016
- Conference dates: 28-29 July 2016
The Fifth Elephant will be held at the NIMHANS Convention Centre, Dairy Circle, Bangalore.
For more information about speaking proposals, tickets and sponsorships, contact firstname.lastname@example.org or call +91-7676332020.
Advanced Deep Learning Workshop – Hands-on
Deep Learning is a hot topic, but has a steep initial learning curve. This workshop is aimed at giving participants ‘hands-on’ experience of a range of deep learning techniques.
While no prior deep learning knowledge is assumed, the content will not be watered down : Even people already deploying models should find material that is new and interesting.
There will be code. Lots of code. To ease the pain, a pre-configured virtual machine will be handed out, so that participants can run it on their own laptops using cross-platform open-source VirtualBox, and avoid a lot of configuration hassles. Bring a laptop with VirtualBox installed!
The workshop will start from the very basics (with a little mathematics), and quickly progress to getting hands-on with open source software including the training of a deep network on simple problems to get ‘warmed up’.
This will be followed by several deeper dives using a pre-built Virtual Machine, running within VirtualBox. Participants will experiment with a much larger pre-trained models, and get an understanding of several application areas, among which are :
Applying a pre-trained model to classify images into previously unseen classes
Reinforcement Learning (inspired by AlphaGo)
While parts of this are very technical, the models (inside the Virtual Machine) are all in Jupyter (fka iPython) notebooks, making interaction straightforward.
The Python libraries that are used are Theano and Lasagne (both on GitHub).
Participants need a laptop with VirtualBox installed (this is cross-platform, and open source). At minimum, the laptop should have 2Gb of RAM and 8Gb of HD available, with the ability to read/install files from a USB key(!) No platform preference.
#####Programming Knowledge Assumed
- The code is all Python-based, using Numpy, Theano (and Lasagne)
- However, understanding every line in the code is not required, since the essence of the material is ‘laid out’ in pre-built Jupyter notebooks
#####Please install following software before coming to workshop
- VirtualBox (https://www.virtualbox.org/wiki/Downloads) is essential;
- Chrome (or Firefox) would be good-to-have too.
In addition, some of the modules make use of images - so having a few of your own images (and some kind of image editing tool for resizing/cropping) could make those sections more ‘personal’ (in the nice-to-have category).
#####Math and ML Requirements
- some matrix mathematics (Google : “matrix and vector multiplication”);
- the idea of using derivatives to minimise functions (Google : “differentiate to find minimum”);
- images being composed of pixels - and what a Photoshop filter does (Google : “Photoshop custom filter maths”); and
- training data vs test data (Google : “training set test set difference”);
Those who want to ‘get ahead’ could Google : “Neural network backpropagation”, and beyond that come terms (all of which will be explained in the workshop) such as “imagenet competition”, “convolutional neural network”, “recurrent neural network”, “deepmind alphago”, “reinforcement learning” and “q-learning”.
Martin has a PhD in Machine Learning, and has been an Open Source developer since 1999. After a career in finance (based in London and New York), he decided to follow his original passion, and now works on Machine Learning / Artificial Intelligence full-time.