The Fifth Elephant 2017

On data engineering and application of ML in diverse domains

##Theme and format
The Fifth Elephant 2017 is a four-track conference on:

  1. Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
  2. Application of Machine Learning (ML) in diverse domains such as IOT, payments, e-commerce, education, ecology, government, agriculture, computational biology, social network analysis and emerging markets.
  3. Hands-on tutorials on data mining tools, and ML platforms and techniques.
  4. Off-the-record (OTR) sessions on privacy issues concerning data; building data pipelines; failure stories in ML; interesting problems to solve with data science; and other relevant topics.

The Fifth Elephant is a conference for practitioners, by practitioners.

Talk submissions are now closed.

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.
  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.

##About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.

We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:

  • Full-length, 40-minute talks.
  • Crisp, 15-minute talks.
  • Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
  • Hands-on tutorials and workshop sessions of 3-hour and 6-hour duration where participants follow instructors on their laptops.
  • Off-the-record (OTR) sessions of 60-90 minutes duration.

##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.

Selection Process Flowchart

We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.

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.

There is only one speaker per session. Entry is free for selected speakers.

##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: June 10
  • First draft of the coference schedule: June 20
  • Tutorial and workshop announcements: June 20
  • Final conference schedule: July 5
  • Conference dates: 27-28 July

For more information about speaking proposals, tickets and sponsorships, contact or call +91-7676332020.

Hosted by

All about data science and machine learning

Anuj Gupta


Learning representations of text for NLP

Submitted Apr 19, 2017

Think of your favorite NLP application you wish to build - sentiment analysis, named entity recognition, machine translation, information extraction, summarization, recommender system. A key step in building it is - using the right technique to represent the text in a form that machine can understand. In this workshop, we will focus on the key concepts, maths, and code behind state-of-the-art techniques for text representation.

This workshop is meant for NLP enthusiast, ML practitioners, Data science teams who work with text data and wish to gain a deeper understanding of Learning representations of text for NLP. This will be a very hands-on workshop with jupyter notebooks to create various representations, coupled with the key concepts & maths that forms the basis of their respective theory.


Machine Learning in Images has had a phenomenal success story. One of the key reasons for it is: Rich representation of data - raw image in matrix form with RGB values.

While in images, directly using the pixel values is a very natural representation. However, when it comes to text, there is no such natural representation. No matter how good is your ML algorithm, it can do only so much unless there is a richer way to represent underlying text data. Thus, whatever NLP task/application you are building, it’s imperative to find a good representation for your text. Motivated from this, the subfield of representation learning of text for NLP has attracted a lot of interest in the past few years.

__ Various representation learning techniques have been discussed at length in literature, but from a practitioner’s point of view, there is a dearth of comprehensive tutorials that provides full coverage with the mathematical explanation and implementation details of these algorithms.__ This workshop aims to bridge this gap. This workshop aims to demystify, both - Theory (key concepts, maths) and Practice (code) that goes into these various representation schemes. At the end of workshop participants would have gained a fundamental understanding of these schemes and will be able to implement embeddings on their datasets.

Course Content:

  1. Old ways of representing text

  2. Introduction to Embedding spaces

  3. Word-Vectors

  4. Sentence2vec/Paragraph2vec/Doc2Vec

  5. Character2Vec

For each of the above representation scheme, we will understand and implement various evaluation and visualization techniques.


Laptop and Lots of enthusiasm.
We will provide pre installed virtual machine which will help you get started without fuss.

Speaker bio

  1. Anuj Gupta is a senior ML researcher at Freshdesk; working in the area NLP, Machine Learning, Deep learning. Earlier he was heading ML efforts at Airwoot(Now acquired by Freshdesk). He dropped out of Phd in ML to work with startups. He graduated from IIIT H with specialization in theoretical comp science.

He has given tech talks at prestigious forums like PyData DC, Fifth Elphant, ICDCN, PODC, IIT Delhi, IIIT Hyderabad and special interest groups like DLBLR. More about him -

  1. Satyam Saxena is a ML researcher at Freshdesk. An IIT alumnus, his interest lie in NLP, Machine Learning, Deep Learning. Prior to this, he was a part of ML group Cisco. He was a visiting researcher at Vision Labs in IIIT Hyd where he used computer vision and deep learning to build applications to assisting visually impaired people. He presented some of this work at ICAT 2014, Turkey.



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Hosted by

All about data science and machine learning