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 email@example.com or call +91-7676332020.
(Workshop) Understanding neural networks by building few from scratch
I have a firm belief that,
- there’s elegant and understandable theory behind neural networks.
- the best way to learn about any ML algorithm is to implement one.
The objectives of the workshop are based on this belief, as follows,
- Help the audience understand the basics of neural networks in intuitive fashion.
- Separate fundamental principles from gory details.
- Understand taxonomy of deep learning (explained below in mind map)
- Learn about neural networks by building them from scratch.
The audience can expect to take away the following after attending the workshop,
- Enough understanding of neural network to make scary looking books or papers
in this area less scary.
- Enough understanding of neural network to call BS on over-hyped headlines and
dissect grandiose claims on their utility.
- Detailed understanding of few deep learning algorithms and architectures by
implementing them from scratch.
- For studying further, which areas to focus on.
The question of whether neural nets are the answer to the question of how brains work, the best known
way of doing Artificial Intelligence or just the current fad to be exploited by the cynical as a new form of
intellectual snake oil, merits serious investigation. The writers tend to be partisan and the evidence
confusing. We shall investigate the need for a mid-life crisis in this chapter.
--- page 251, Michael Alder, An Introduction to Pattern Recognition
Rather than thinking of neural networks as a document handed down from top of a
mountain by gods, we look at the history of development of neural networks. We will
go through questions like what problems were they meant to solve,
critical milestones in the development etc. This hopefully will reveal some connections
(pun intended!) with other models in machine learning.
The audience is expected to know certain basics of machine learning (supervised vs.
unsupervised models, gradient descent algorithm, cost functions). We will do a very
fast revision of these topics.
We will implement simple single hidden layer neural network for classification in this
part. The implementation will be done using with using simple for loops and basic maths
as far as possible.
When I started reading about neural network, jargon or terminologies always overwhelmed me.
I couldn’t understand the difference between convolutional neural networks and layer wise
pre-training, nor could I decide if it even made sense to talk about the two together.
In this part of workshop, we will dissect neural networks (and jargon) from various angles: by
architectures, by optimization strategies, by application domains.
In this section, depending upon time we will implement 1 or 2 ideas from the so called deep learning
neural networks. Again, the idea will be to build everything from scratch and shun the use of libraries.
We could cover few of the following,
- convolutional neural nets
- recurrent nets or long short term memory networks
- word vector embeddings / word2vec
- layer wise pre-training / unsupervised pre-training, optimizations
This will be open interaction and question answer section.
This is not a beginner workshop. I expect the participants to know the
following at the least. More the better!
- Basics of machine learning (supervised Vs. un-supervised)
- Understand gradient descent algorithm, cost functions.
- Quick review of high school calculus.
- You should know basic programming
- Reading and Writing files
- Flow controls (if-else)
- Looping constructs like for loop, while
- Variable assignment
- In other words, you should have programmed at least few hundred lines
in any mainstream programming language.
- The implementation choice for this workshop will be Python.
- Laptop (operating system of your choice), charged battery + charger.
- Python installed on the laptop + IDE of your choice including console.
- No hard choice between python 2 Vs python 3.
- Numpy and scipy stack installed for python.
- Make sure the scipy stack for appropriate python version is installed.
- It won’t be possible to provide installation support at the time of workshop.
So all requirements should be pre-installed.
- It comes pre-installed on most Linux machines.
- Overall procedure is documented on python website.
- Scipy Stack
The links to the datasets to be used during the workshop will be posted here about 2-3 weeks before workshop.
I am heading the data science and analytics team at sokrati.com, an advertising technology startup based out of Pune. I’ve spent almost 6 years in applying machine learning to various domains (advertising, banking, telecom). I have conducted a number of hands on workshops and talks on various topics in machine learning for audience of data science beginners. I am proficient in use of R and Python for the data science domain and have some hands on experience with Clojure. I also serve as a data science mentor at Sprinboard.com, an education startup, primarily focussed on data science education.
- Link to the mind map of this proposed workshop is here.(http://www.slideshare.net/HarshadSaykhedkar/neural-networks-mind-map)
- Link to my previous workshop on machine learning basics is here.(https://hasgeek.tv/fifthelephant/2014-workshops/959-real-world-machine-learning)