The Fifth Elephant 2017
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
##Theme and format
The Fifth Elephant 2017 is a four-track conference on:
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:
##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:
##Selection Process
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:
##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Hosted by
Ramanan Balakrishnan
@ramananbalakrishnan
Submitted Apr 25, 2017
With AI research and machine learning systems growing at great speed, companies require significant effort to keep up or risk losing their relevance in this brave new world. The new tide also brings with it numerous tools to tackle previously intractable problems. However, there does seem to exist a gulf between appreciating these developments and subsequently deploying them. Despite the global push to democratize machine learning, the steps prescribed don’t align with the fuzzier problems that need solving.
As a startup focused on organizing the world’s ecommerce data, Semantics3 has faced its fair share of challenges. To tackle numerous problems covering categorization, feature extraction, cross-domain product matching and price tracking, we have had to incorporate multiple modern techniques into our workflows.
Going over our experiences, I would like to share the broader questions (not whether you need a CNN, GAN or TROL) that need to be considered. Datasets, frameworks and deployment practices - are just some of the topics I wish to touch upon. The talk is almost a recollection of our journey, when moving machine learning from practice to production in an ecommerce-centric environment.
A short introduction to the topics that are to be covered. Additional context about the machine learning problems in the domain of ecommerce data will be presented. This section sets the stage for discussing the machine learning “pipeline” to be built for solving the various problems.
Having high-fidelity datasets is crucial when starting on any machine learning problem. Balanced classes, representative coverage of ground truth, and adversarial examples, all need to be considered before jumping in with the modelling.
Topics covered:
Once the datasets have been prepared, the actual “fun” can begin - experimentation is the name of the game. This section will be present an overview of the landscape together with processes that help weigh available options.
Topics covered:
The 99.8% accurate model is not of much use, unless it can be integrated into systems meant to solve the actual problem. By constantly keeping these integration goals within sight, the aim is to convey the point that early and good enough is often better than late and perfect.
Topics covered:
While pushing the state-of-the-art in machine learning might sound interesting, it is still important to maintain focus on its applicability within desired domains. Together with an overview of the points covered, a few concluding remarks will be presented.
Aimed at machine learning enthusiasts who wish to get started with applying machine learning techniques across various domains.
I am a member of the data science team at Semantics3 - building data-powered software for ecommerce-focused companies. Over the years, I have had the chance to dabble in various fields covering data processing, pipeline setup, database management and data science. When not picking locks, or scuba diving, I usually blog about my technical adventures at our team’s engineering blog.
https://docs.google.com/presentation/d/17AQ0boRinVt1HhTUiufYVz7SE_Q1SRu5AUW6EI_bwbs/pub
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
Hosted by
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