Anthill Inside 2017
On theory and concepts in Machine Learning, Deep Learning and Artificial Intelligence. Formerly Deep Learning Conf.
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu
28 Fri
29 Sat 09:00 AM – 05:40 PM IST
30 Sun
##About AnthillInside:
In 2016, The Fifth Elephant branched into a separate conference on Deep Learning. Anthill Inside is the new avataar of the Deep Learning conference.
Anthill Inside attempts to bridge the gap bringing theoretical advances closer to functioning reality.
Proposals are invited for full length talks, crisp talks and poster/demo sessions in the area of ML+DL. The talks need to focus on the techniques used, and may be presented independent of the domain wherein they are applied.
We also invite talks on novel applications of ML+DL, and methods of realising the same in hardware/software.
Case studies of how DL and ML have been applied in different domains will continue to be discussed at The Fifth Elephant.
https://anthillinside.in/2017/
##Format:
Anthill Inside is a two-track conference:
We are inviting proposals for:
You must submit the following details along with your proposal, or within 10 days of submission:
##Selection Process:
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 to start evaluating your proposal.
You can check back on this page for the status of your proposal. We will notify you if we either move your proposal to the next round or if we reject it. 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.
A speaker is NOT confirmed a slot unless we explicitly mention so in an email or over any other medium of communication.
There is only one speaker per session. Entry is free for selected speakers.
We might contact you to ask if you’d like to repost your content on the official conference blog.
##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.
Please note, we will not evaluate proposals that do not have a slide deck and a video in them.
Hosted by
Ananth Krishnamoorthy
@akrishnamoorthy
Submitted Apr 27, 2017
As practitioners in Deep Learning, we often want to understand emerging areas by prototyping and modeling. While there are many python libraries for deep learning, Keras stands out for it’s simplicity in modeling.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. It provides a deep learning library that (1) Allows for easy and fast prototyping (2) Supports both convolutional networks and recurrent networks, as well as combinations of the two, and (3) Runs seamlessly on CPU and GPU.
In this talk, we explore the basic elements of DL and different DL architectures using Keras. To facilitate this discussion, we take three seemingly different applications: (1) Image Recognition, (2) Control a Car (Simulation), and (3) Speech Recognition
The focus of this talk is on modelling, and that is where we shall spend the bulk of our time. We will quickly discuss the basics and then look at applications, stepping through the core Keras code visually, and do a few demos. This is NOT a workshop.
Intro to Deep Learning
What is a Deep Learning Architecture?
Keras Basics
Application 1: Image Recognition
Application 2: Control a Car
Application 3: Speech Recognition
Machine Learning practitioners, Deep learning beginners, enthusiasts
Ananth Krishnamoorthy Ph.D. specializes in applying analytical techniques based on mathematical optimization, machine learning, discrete event simulation, and time series analysis, to real world business problems across various industry sectors. He has delivered several business consulting, analytical solution development, and technology implementation projects over the last 17 years.
Ananth is the co-founder of rorodata, a startup that is building a cloud based data science platform. He is also head of Hypercube Analytics, an analytics consulting company. Ananth holds a Ph.D. in Industrial Engineering and Management from Oklahoma State University
https://www.slideshare.net/ananthkrishnamoorthy/keras-a-versatile-modeling-layer-for-deep-learning
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