Deep Learning Conf 2016

A conference on deep learning.

Deep Learning is a new area of research that is getting us closer in achieving one of the primary objectives of Machine Learning – Artificial Intelligence.
It is used widely in the fields of Image Recognition, Natural Language Processing (NLP) and Video Classification.

Format

Deep Learning Conf is a single day conference followed by workshops on the second day. The conference will have full, crisp and lightning talks from morning to evening. The workshops on the next day will introduce participants to neural networks followed by two tracks of three-hour workshops on NLP and Computer Vision / AI. Participants can join either one of the two workshop tracks.

Tracks

We are looking for talks and workshops from academics and practitioners of Deep Learning on the following topics:

  • Applications of Deep Learning in software.
  • Applications of Deep Learning in hardware.
  • Conceptual talks and cutting edge research on Deep Learning.
  • Building businesses with Deep Learning at the core.

We are inviting proposals for:

  • Full-length 40 minute talks.
  • Crisp 15-minute talks.
  • Lightning talks of 5 mins duration.

Selection process

Proposals will be filtered and shortlisted by an Editorial Panel. Along with your proposal, you must share the following details:

  • Links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience.
  • Blog posts you may have written related to your proposal.
  • Outline of your proposed talk – either in the form of a mind map or a text document or draft slides.

If your proposal involves speaking about a library / tool / software that you intend to open source in future, the proposal will be considered only when the library / tool / software in question is made open source.

We will notify you about the status of your proposal within two-three weeks of submission.

Selected speakers have to participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you prepare for speaking at 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).

Commitment to open source

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.

Key dates and deadlines

  • Proposal submission deadline: 31 May 2016
  • Schedule announcement: 15 June 2016
  • Conference dates: 1 July 2016

Venue

CMR Institute of Technology, Bangalore

Contact

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

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Sundara R Nagalingam

@nsundarrl

Recent advancements in Deep Learning techniques using GPUs.

Submitted May 31, 2016

NVIDIA has for long been a pioneer in providing the tools to facilitate deep learning. At the heart of deep learning lies the need to train Deep Neural Networks and then have these DNNs perform complex compute tasks in the shortest possible time. NVIDIA has made huge advances in developing a comprehensive software development kit, aimed at helping developers train DNNs at speeds that keep beating previous records. The solution includes cuDNN, cuSPARSE and cuBLAS libraries, DIGITS for training and NCCL to scale up the performance across multiple GPUs. Combined with the immense power of Tesla GPUs built on the newly launched Pascal architecture, this entire combination helps achieve the end goal of bigger and better DNNs driving deep learning problems across multiple domains. Customers such as Facebook, amongst many, are harnessing NVIDIA’s deep learning solutions to provide end user impact via their applications. In India, smart startups leverage our technology to develop intelligent solutions in the consumer space, intelligent video analytics, security, smart search and many more.

Outline

Evolution of Deep Learning (DL) techniques - Over view of modern DL stack (software and hardware)- Advances in GPU computation and how it helps to dramatically bring down DL training time - Modern tools CuDNN, CuSPARSE, cuBLAS, DIGITS-NCCL to improve intranode and internode scaling - Success Stories - DL Ecosystem in India from Enteprise and Startups perspective.

Speaker bio

Mr. Sundara R Nagalingam is the Head of Manufacturing and Energy businesses for NVIDIA India. He is also responsible for managing the Deep Learning business ecosystem for the company.

He has twenty years of experience in solutions involving Visual Computing, Virtualization and High Performance Computing. He also has exposure to the work cultures of multiple countries in the Asia Pacific region.

He has a strong technical background and his areas of interest include Deep Learning, Big Data Analytics, IoT and Automotive Solutions.

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Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more