1st July 2016, Bangalore
Status: Open for post-event feedback

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:

We are inviting proposals for:

Selection process

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

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

Venue

CMR Institute of Technology, Bangalore

Contact

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

Confirmed sessions

# Speaker Section Level +1 Submitted
1 Expresso - A user-friendly tool for Deep Learning
Jaley Dholakiya (@jaleydholakiya) Crisp talk Beginner 1 0 Sat, Jun 11
2 Deep Learning for Computer Vision
Anand Chandrasekaran (@anandchandrasekaran) Workshop Intermediate 4 0 Wed, Jun 8
3 Deep learning for computational pathology
Neeraj Kumar (@neerajkumar89) Full talk Intermediate 2 0 Mon, Jun 6
4 Recent advancements in Deep Learning techniques using GPUs.
Sundara R Nagalingam (@nsundarrl) Sponsored talk Intermediate 0 0 Tue, May 31
5 Slot-Filling in Conversations with Deep Learning
Nishant Sinha (@ekshaks) Crisp talk Intermediate 5 0 Tue, May 31
6 Making Deep Neural Networks smaller and faster
Suraj Srinivas (@surajsrinivas) Crisp talk Intermediate 5 0 Tue, May 31
7 Applied Deep Learning
Abhishek Thakur (@abhishekthakur) Full talk Intermediate 16 2 Sun, May 29
8 Challenges & Implications of Deep Learning in Healthcare
Suthirth Vaidya (@suthirth) Full talk Intermediate 5 0 Tue, May 24
9 Deep Dive Into Building Chat-bots Using Deep Learning
Vijay Gabale (@vijaygabale) Full talk Intermediate 12 0 Mon, May 23
10 Deep learning: A convoluted overview with recurrent themes and beliefs.
Anand Chandrasekaran (@anandchandrasekaran) Full talk Intermediate 2 0 Fri, May 20
11 Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Arjun Jain (@stencilman) Crisp talk Advanced 7 1 Wed, May 11
12 Residual Learning and Stochastic Depth in Deep Neural Networks
pradyumna reddy (@pradyu1993) Crisp talk Intermediate 12 0 Fri, May 6
13 Introduction to Deep Learning for Natural Language Processing
Nischal HP (@nischalhp) Workshop Intermediate 13 0 Fri, Apr 29

Unconfirmed proposals

# Speaker Section Level +1 Submitted
1 Object Detection using deep convolutional network
Koustubh Sinhal (@koustubh) Crisp talk Advanced 2 1 Tue, May 31
2 Text made Understandable by Machines
Ashish Kumar (@ashish122) Full talk Intermediate 28 2 Mon, May 30
3 Deep learning for Image and Feature recognition
Hemant Jain (@jainhemant) Full talk Intermediate 31 2 Mon, May 30
4 Debugging deep nets
Vivek Gandhi (@vivgandhi) Full talk Intermediate 12 1 Sun, May 29
5 Sequence learning
Rajarshee Mitra (@rajarsheem) Full talk Intermediate 15 0 Sat, May 28
6 Deep Learning with MATLAB : Real-time Object Recognition and Transfer Learning
Sunita John (@sunjoh) Crisp talk Intermediate 7 0 Thu, May 26
7 Learning to play games / Deep Reinforcement Learning
Utkarsh Sinha (@liquidmetal) Full talk Intermediate 12 0 Wed, May 25
8 Building DeepNets using Keras
Anuj Gupta (@anujgupta82) Workshop Intermediate 11 0 Tue, May 24
9 Activations, Objectives and Optimisers - Nuts & Bolts of a DeepNet
Anuj Gupta (@anujgupta82) Full talk Intermediate 12 0 Tue, May 24
10 Events Build Build 2016 Microsoft Cognitive Services: Build smarter and more engaging experiences
Abhishek Narain (@nabhishek) Full talk Beginner 1 0 Mon, May 23
11 Automated Interior Designing using Bayesian networks
Aakanksha Bapna (@aakankshabapna) Crisp talk Intermediate 5 0 Mon, May 16
12 Debugging DeepNets - practitioners black book
Anuj Gupta (@anujgupta82) (proposing) Full talk Advanced 4 0 Fri, May 13
13 Practical Deep Learning
Arthi Venkataraman (@arthi) Full talk Intermediate 3 0 Fri, Apr 15