Workshops for PySangamam 2018

4 SEP 2018

While the talks and posters are the main agenda of the conference, we do realize people would like to try things hands-on, by interacting with experts in the field. So, as part of PySangamam 2018, we have the following workshops scheduled.

  1. Computer Vision Through the Ages - Day 1, Afternoon
  2. Concurrent Programming in Python - Day 2, Forenoon
  3. Introduction to Pandas & Scaling Pandas for large Datasets - Day 2, Afternoon

Each workshop can accommodate a maximum of 60 attendees. Attendees can sign up for the workshop during the registration process on Day 1. Please note that an attendee can take up only one workshop to ensure that there is a fair sharing of the available seats.

Workshop Details

Detailed description of the workshop, and information about the speakers are provided below.

Computer Vision Through the Ages


Day 1: 3:00 PM to 5:30 PM


  • Knowledge of basic probability
  • Vector algebra - vectors, matrices, matrix multiplication
  • Idea of convolutions would be a bonus, but not necessary

Software Requirements

  • Docker CE 16.0 and above

Content Outline

  1. What is computer vision? (15 min)
    • Teaching computers to see. Difference from image processing.
    • Image and video representations in computers (pixels, colour spaces).
    • Why is computer vision hard?
    • Examples of different computer vision problems (detection, segmentation, shape from X, etc).
  2. Computer Vision through the ages (30 min)
    • The generic pipeline (hand engineered feature approach).
    • Gradient Based Methods (HOG, HAAR).
    • Scale invariant Feature Matching Algorithms (SIFT and SURF).
    • Other tasks with classical Computer vision.
    • Issues with classical computer vision.
  3. Rise of learning methods (15 min)
    • Youtube cats, training on CPUs.
    • SAIL - the era of GPUs.
    • In a nutshell, ML+ NN = DL.
    • Alexnet and the impact of Imagenet.
  4. The Deep Learning Era in flow (45 min)
    • Introduction to Machine Learning, benefits of end-to-end solutions.
    • Using ML algorithms for Computer vision tasks (Hello world (MNIST with keras)).
    • Feed forward neural networks for MNIST classification.
    • Introduction to Convolutional Neural Networks (CNNs).
    • Using CNNs for Image Recognition .
  5. Concept of Transfer Learning (30 min)
    • Why transfer learn; Less data? Not always a problem.
    • Concept of Bottlenecks.
    • A hands on example.
    • Practitioner advice; Closing comments.

Speaker: Varun Sundar

I’m Python junkie by heart and passionate about using machine intelligence to impact our surroundings. As a third year undergraduate in the Indian Institute of Technology Madras, and as a part of the Computer Vision and Intelligence group, I have been grateful to have been a part of a lively peer group. When not scratching my head over tensorflow’s various confusing APIs, I enjoy watching tennis and an occasional trek.

Expertise on subject of the talk: Our blog articles may be found at ([]. We organise sessions on computer vision and Deep learning regularly at IIT Madras. The repository for the same (session content history) may be found here.

Speaker: Lokesh Kumar

Lokesh is a 3rd-year student at IIT Madras. He currently co-heads the CVI group, CFI. He uses Python for Computer Vision, Deep Learning, and Language Analysis. In DeTect technologies, he has worked on automating the chimney and stack inspections using Computer Vision and on on-Board vision-based processing for drones. His interest in Python began during his stay at IIT Madras, from institute courses to CVI projects like face recognition, hand gesture control of bots, etc.

Concurrent Programming in Python

This Python programming workshop provides insight on how to develop concurrent programs in Python.

This workshop will focus on creating generators and co-routines using Python 2 and Python 3 and also the new async and await feature introduced in Python 3.5.


Day 2: 10:30 AM to 1:00 PM


  • Knowledge of basic Python syntax and semantics

Software Requirements

  • Python 3.6 and above

Content Outline

  • Concurrency Vs Parallelism: Choosing generator/coroutines Vs Threads/Processes
  • Introduction to generators
  • Generators as Iterables
  • Creating Generators using the yield statement
  • Co-routines
  • Examples:
    • Creating multitasking Greenlets using co-routines
    • Creating concurrent execution pipelines using co-routines
  • Creating co-routines in Python 3.5 using async definitions
  • Using await expressions
  • Realword examples of frameworks using concurrency patterns:
    • gunicorn
    • vibora
    • sanic
    • aiohttpd
    • gevent
    • twistedmatrix
  • Threads and Processes (*subject to time availability)
    • Multithreading using Thread class
    • All about Python GIL
    • Circumventing GIL limitations with cython and numba
    • Multiprocessing using Process class
    • Threads Vs Process: design choices and trade-offs

Speaker: Chandrashekar Babu

FOSS Technologist and corporate trainer, with 22+ years of technology experience on Linux and other Free/Open Source Software (FOSS).

I am passionate about two things in life - to learn something new, without any barrier; and then to train what I learn to others :-). I spend a lot of time in learning new technologies, new trends and devote a lot of time in front my computer - installing and playing around with various free/open source software, toy operating systems, learning new libraries, tools, programming languages and at times reading articles, books and journals related to the same.


Introduction to Pandas & Scaling Pandas for large Datasets

Pandas is a multi purpose data analytics tool which can be used for simple, complex and time series aggregations. We have been using pandas for all our data science and few of our data transformation pieces for a long time now. We will teach the basics and advanced aggregations with Pandas. We will also teach newer ways to scale pandas for solving medium size data engineering problems.


Day 2: 3:00 PM to 5:30 PM


  • Basic Python concepts

Software Requirements

  • Anaconda (Python 3.6)

Content Outline

  1. Basics
    • Very quick introduction to pandas and jupyter notebook
    • Pandas Series
    • Pandas Dataframe
    • Reading multiple formats and creating
    • Simple dataframe operations
  2. Aggregations & Advanced Indexing
    • Group-bys in pandas world
    • Simple & complex aggregations
    • Pivots & multi indexes
  3. Visualization
    • Basic visualizations with dataframe
  4. Optimizing & Scaling
    • Optimizing size of dataframe
    • Thinking in terms of vectorized operations
    • Introduction to Dask
    • Introduction to numba

This session is heavily inspired from Alexander Hendorf’s Data Wrangling with pandas session.

Speaker: Raghotham Sripadraj

Raghotham Sripadraj is Principal Data Scientist at Treebo Hotels. Previously, co founder and data scientist at Unnati Data Labs. Drawing on his deep love for data science and his passion for teaching, Raghotham has conducted workshops and given talks at a number of data science conferences around the world. Apart from getting his hands dirty with data, he loves traveling, pink floyd, and masala dosas.

Speaker: Jasjot Singh Mann

Jasjot Singh Maan is a Data scientist at Treebo Hotels. He is mostly found working on various datasets to draw insights for business.

Speaker: Chaitanya Sangani

Chaitanya Sangani is a Data Scientist at Treebo Hotels. Building data products by the day, scraping data by the night.