Selected Talks for PyConf Hyderabad 2017 have been announced. Please Check the Confirmed Proposals section for the Selected Talks
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).
- Background image/wallpaper shouldn’t contain company name/logos.
- For any questions, please write to email@example.com.
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
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.
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.
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.
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
- Scientific Computing
- Cloud computing with Python
- 31st August, 2017 : Deadline for Proposal Submission
- 16th September, 2017 : Talk selection and announcement
Dimensionality Reduction and Principal Component Analysis
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.
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.
- Matrices and Matrix Multiplication
- Simple Prediction Algorithms like linear regression
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.