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.
Introduction to Deep Learning
In fields like computer vision, speech recognition and natural language processing, deep learning has produced state-of-art results. And they are showing lot of promise in other fields too.
This workshop will provide an introduction to deep learning. It would cover some of the common deep learning architectures, advantages and concerns, along with some hands-on.
- What is deep learning?
- Motivation: Some use cases where it has produced state-of-art results
- Supervised learning (multi-layer perceptron, deep convolution networks)
- Unsupervised learning (autoencoders) time permitting
- Overview of a few libraries and the impact of GPUs (Some practical thoughts on hardware and software)
- Hands-on modeling a simple classification problem
We would be using Python-based libraries to do the hands-on.
The data and software requirements would be posted onto the github repository one week prior to the workshop.
The repository for this workshop:
EDIT(13July2015): Software and data installations are posted on the repository. Please install them prior to the workshop.
If you’ve never built a predictive model, this is probably NOT the ideal workshop to attend. Having said this, the attendee should, at the bare minimum, understand the following terms:
- Bias and Variance
- Train, Test and Validation sets
- Cross-validation, grid-search, hyperparameter optimization
- Measuring model accuracy (Precision, Recall, F1 score, Area Under Curve)
- Supervised and Unsupervised learning
Also, to follow the hands-on, the attendee should have had some programming experience (Reading files, performing some data manipuation on them, loops, conditional expressions)
We would be using Python stack for the hands-on. Knowing Python is a plus, but not mandatory.
Question we repeatedly get asked: Do I need a laptop with GPU? Do I need a powerful processing machine? Do I need a lot of RAM? Do I need a cloud compute account? Fret not. We would do something really small-scale. A laptop with 4 GB RAM should suffice.
I use a Windows machine? We haven’t used a Windows machine in a while and so, would be a challenge to support bugs/issues in Windows. We recommend installing a linux VM.
Data and the Software libraries? Please check the github repository a week before the workshop. Data and the library installation instructions would be posted there.
I dont know Python! That’s perfectly fine. The concepts aren’t tied to any language. The constructs used in the code would be as simple as possible.
Bargava Subramanian is a Senior Statistician(Data Scientist) at Cisco Systems, India. He has a Masters from University of Maryland, College Park, USA.
Raghotham is a full-stack developer at RedMart. He has a Masters from BITS, Pilani. In his previous role, he was instrumental in architecting an analytics platform for a wearable devices company.
- A crash course in Classification Algorithms (https://fifthelephant.talkfunnel.com/2014/1158-machine-learning-using-r-crash-course-in-classific) - Workshop, Fifth Elephant 2014
- Mind map for the workshop (https://www.dropbox.com/s/yoa3ckl9ayo3sa3/MindMap - Intro Deep Learning - workshop.pdf?dl=0)