Introduction to Deep Learning

Jul 2015

13 Mon

14 Tue

15 Wed

16 Thu 08:30 AM – 06:35 PM IST

17 Fri 08:30 AM – 06:30 PM IST

18 Sat 09:00 AM – 06:30 PM IST

19 Sun

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NIMHANS Convention center

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

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.

Workshops

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.

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The Fifth Elephant

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

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This submission has been added to the schedule

Introduction to Deep Learning

Submitted May 19, 2015

Section: Workshop Technical level: Intermediate

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.

Outline

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:
https://github.com/rouseguy/intro2deeplearning/

EDIT(13July2015): Software and data installations are posted on the repository. Please install them prior to the workshop.

Requirements

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.

FAQs

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.

Speaker bio

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.