Machine Learning, Distributed and Parallel Computing, and High-performance Computing are the themes for this year’s edition of Fifth Elephant.
The deadline for submitting a proposal is 15th June 2015
We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.
Track 1: Discovering Insights and Driving Decisions
This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:
- Statistical Visualizations
- Unsupervised Learning
- Supervised Learning
- Semi-Supervised Learning
- Active Learning
- Reinforcement Learning
- Monte-carlo techniques and probabilistic programming
- Deep Learning
Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.
Track 2: Speed at Scale
This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:
- Distributed and Parallel Computing
- Real Time Analytics and Stream Processing
- MapReduce and Graph Computing frameworks
- Kafka, Spark, Hadoop, MPI
- Stories of parallelizing sequential programs
- Cost/Security/Disaster Management of Data
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 license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize 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.
If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.
Building Data Products for Small / Mid-Sized Data
Understand the process I and Kevin Gates went through in building www.seeingtheair.com, a hackathon data product to compare Air Quality in various cities. Audience will have an appreciation for - Data Extraction, Exploration phase along with building an Web App and some intuition for Data Viz. I intend to show Python Code behind the app in this talk.
At a recent Data Viz. Hackathon, Data Canvas (map.datacanvas.org), we were asked to tell a story based on the sensors deployed at various cities (San Francisco, Geneva, Boston, Bangalore, Shanghai, Singapore). I along with Kevin Gates wanted to tell a story around Air Quality index and built www.seeingtheair.com.
We will talk about the process of building this data product:
- Identifying the story we want to tell through Data Exploration using IPython, Pandas and other Python Data Tools.
- Building an App that serves the data
- Some comparison on Data Viz. choices
I will be using lot of code (Python and some JS) snippets throughout the talk.
This talk is not about Big Data, but making sense of data that’s all around us.
I am a Software Engineer making applications for fun and profit. Over the last few years, I have done my own Startup for a couple of years building e-commerce apps for small businesses to automatically move the products in their online stores. I mostly use Python for my Web and data adventures.
- App: www.seeingtheair.com
- Data: map.datacanvas.com
- Winning Entries: http://www.swissnexsanfrancisco.org/media/latest-news/dataartchallengewinners/ (You will see our entry under the Maker Prize section)