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.
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.
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
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.
Anatomy of RDD : A Deep dive into Spark RDD Data structure.
RDD is the core abstraction of Apache Spark. So understanding RDD in depth is very
crucial to use spark very effectively. This talks aims to take audience a deep
dive into RDD to make them understand why it’s so powerful.
This is an Advanced talks aimed toward people who already know Spark. This talk
tries to deconstruct RDD abstraction to peek inside. We will be discussing about
- Immutability and Distribution
- Partition API’s like mapParittions, lookUp etc
- Implementation of Laziness
- RDD dependency hierarchy
- Transformation and Action implementation
- Caching implementation
All the above topics are discussed with real code.
- Prior experience of Working with Spark
Madhukara phatatak is a Bigdata consultant @ Datamantra. He has been actively working in Hadoop,Spark and its ecosystem projects from last 5 years.
He was lead developer of Nectar, a ML library for hadoop.He also contributed to hadoop source code to improve cyclic checks in Jobcontrol api.With raise of Apache Spark, he with his team has open sourced courseera machine learning course examples on spark here. He blogs on spark here. Also he runs a Spark meetup group in Bangalore.
- Apache Spark Architecture : https://www.youtube.com/watch?v=65aV15uDKgA
- Extending Spark API : https://www.youtube.com/watch?v=2T1Pub7IxCU
- MindMap : https://drive.google.com/file/d/0B_j4gJdtrf_QMFJyREF6SVJNTHc/view?usp=sharing