Guidelines for Proposal Submission

Following are the guidelines for proposal submission

Please mention type of Proposal as given below in the Title of the Proposal.
The proposal should have an objective with clear expectation for the audience.
The Proposal description should be short and to the point.
The proposal should have proper prerequisites like environment setup, library version.
No proposal will be selected without a link to appropriate session content like presentation, pdf, code snippets etc.
Proposal content should adhere to code of conduct.
Proposal content links can be updated later.
Proposal content shouldn’t have a company name throughout the content. Mention of the employer is allowed only at the beginning of the content (presentation/pdf).
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For any questions, please write to contact@hydpy.org.

Proposal Types

We have three kind of Proposals - General Talks, Lightning Talks and Workshops. Please mention the Proposal type in the Title of the Proposal. Give a Title like Proposal Type : Proposal Title

General Talks

These are the traditional talk sessions scheduled during the first day of conference. They will be conducted on Day 2 of Conference, Sunday, 8th Oct. The length of these tracks are 45 minutes.

Lightning Talks

These are short length talks that will be conducted on Day 2 of Conference, Sunday, 8th Oct. The time limit is 5 minutes. But we can extend it depending on number of talks submitted.

Workshops

As with the talks, we are looking for Workshops that can grow this community at any level. We aim for Workshops that will advance Python, advance this community, and shape the future. Each session runs for 6 full hours plus a break for lunch. There will be 2 workshops going parallely on Day 1 of Conference, Saturday, 7th Oct in the same venue that hosts the main conference. Workshop I is aimed for Begineers while Workshop II is a Advaced Session aimed for Professionals.

Themes and Topics

These will be the themes and topics

Core Python and Python 3 features
Concurrent and Asynchronous programming in Python
Data Science and Analysis
Web Development
Python and IOT
Functional Programming
Artificial Intelligence
Continuous integration and Deployment
Testing
Scientific Computing
Cloud computing with Python

Important Dates

31st August, 2017 : Deadline for Proposal Submission
16th September, 2017 : Talk selection and announcement

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Dimensionality Reduction and Principal Component Analysis

Submitted Aug 23, 2017

Technical level: Intermediate

PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by some projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. It is one of the most popular methods that is used for Dimensionality Reduction.

Key takeaways: In this session we will understand the theoretical concepts behind PCA, when and how to use PCA, how to achieve dimensionality reduction, and how it benefits us using real world data.

Outline

Normally when we are applying any of the machine learning concepts, we need to deal with a lot of matrices. Each matrix may have a lot of features or dimensions and then we will need to do a lot of computation. It may be prohibitive to run all the computations in a production environment, not counting the added problem of overfitting. In many occasions it is also very useful to visualise the data. Due to our limitations as human beings, we are not able to visualise higher dimensions. For these reasons we need to resort to Principal Component Analysis or PCA to reduce the dimensions in our data-set. In this talk you will learn

What is Principal Component Analysis and why you should be interested in this?
The math behind principal component analysis and why it works the way its supposed to work?
How to select principal components?
Implementing this in production using sklearn

Additional and Optional(if time permits)

How to plugin PCA to an existing production application.

Requirements

Knowledge on

Matrices and Matrix Multiplication
Pandas
Numpy
Sklearn
Bokeh
Simple Prediction Algorithms like linear regression

Speaker bio

Hello, I am a software engineer/data scientist working for a consulting firm called Nineleaps. Currently I am working on a project where we are trying to apply machine learning algorithms to various medical problems and the pharmaceutical industry at large. I also have a podcast on various developer topics called Flawcode. I love talking about machine learning and software engineering and you can send me a hi at @alt227Joydeep.