Welcome to PyCon India CFP
Technical talks are the most important event at PyCon India, the core of the conference essentially. Two of the four days are dedicated to talks. Talks are short lectures (30 min slot) supported by a presentation. Speakers come from the Python community.
Talks are selected through a CFP (Call For Proposals) process. Interested members of the community propose their talks. An editorial panel designated by the organizers makes the selections. The 2018 edition of the conference saw some 267 proposals, of which 31 were selected.
CFP applications from the previous years can be seen here.
Salient Points
- There are __three parallel tracks__
- Talk duration is __30 mins__ (25 mins for the talk, 5 mins - Q&A)
- CFP closes on __1st July 2019__
- Schedule shall be released on __1st September 2019__
- Talks will be presented on __12th and 13th October 2019__
What to Propose
Anything of interest to Python programmers is welcome. However, there are a few topics that we feel might be great -
Lessons from using Python in your project. Did you find something against conventional wisdom? Something confirming conventional wisdom ? Do you have advise for people solving similar problems? Eg - I tried Python for video processing, or in my medical imaging project, and here are the lessions.
Something you're doing to make the language/ecosystem better. Writing a library to solve an interesting problem ? Or have some new ideas on optimization.
Something you learned from a different language that may be useful to Python community. How about a type system? Or patterns from functional programming. Or logic programming maybe?
Thoughts on tech culture and living. Ideas on improving diversity and inclusiveness. On programmers’ physical and mental health. On getting better at productivity. On workplace issues. Anything that can make an impact, especially if you have used Python for any of the above or have seen someone using Python.
And if you don't get any ideas along these lines, try plain and simple teaching. Pick up an niche topic (maybe a recent technology, or a scientific paper), and help us learn. A well delivered lecture even at a beginner level is often well received.
The Review Process
- Authors should propose their talks using the CFP application
- CFP volunteers review the proposals for completeness
- Once the proposals are ready, they are be reviewed by a panel of experts
- If the proposal does not look complete, or the reviewers need clarifications, the author is notified via comments
- The panel of experts finally vote on the proposals
- A pre-final shortlist is eventually prepared based on the votes
- The shortlisted proposals go through a round of rehearsals (more details in section below)
- A final list is created and published.
Rehearsals
Shortlisted speakers will be expected to participate in rehearsal sessions. Rehearsals will be done via teleconferencing, where the speaker shall give a mock run of their talks in a time-bound manner. The audience will consist of volunteers, reviewers and possibly other speakers. The speakers will be given feedback if necessary.
The point of this exercise is to make sure speakers are ready with their talks ahead of time. And also, to make sure they can finish the talk in the stipulated time. It is useful for the speakers too as they'd get feedback on the content delivery and presentation.
Participation in the rehearsal sessions is likely to be a required step - chances of an unrehearsed talk making it to the final stage are substantially lower.
Diversity
We in the Python community believe in making our community more diverse. This means we are encouraging content from diverse walks of life. This also means we want to improve participation from under-represented groups.
Our goal is to maximise content from under-represented groups. You can help us by encouraging your friends, family and colleagues to submit talks. You can also help by mentoring.
Also note that we have a strict code-of-conduct. This is to make it clear, in intent and practice, that we are committed to making the conference a pleasant, welcoming and harassment free experience for everyone, especially for under-represented groups.
Best Practices for Speakers
1. Apply
Even if you have a vague idea, submit a proposal. We're available for help with ideas and feedback (contact information is in the section below). Don't worry about communication skills or English - we are there to help with that too. And our focus is more on the content.
2. Make it detailed
Add as much detail as possible to the proposal. Add the presentation slides if you already have one. Add a short minute video giving a summary of the proposal. More detail helps reviewers make better judgement.
3. Propose early
We will start the review process as the proposals come in, and not at the end. Proposals submitted early will get more attention and feedback
4. The code of conduct
Take a look at the code of conduct, and be mindful of it. The gist is, be nice and avoid using sexist language.
We've put together a set of detailed best practices - take a look. It also contains links to some well written proposals from previous years.
Questions and Discussions
Ping us on Gitter
Or contact the coordinators through email:
Naren - narenravi92@gmail.com
Abhishek - zerothabhishek@gmail.com
The team: cfp@in.pycon.org
Proposal Sections
- Game Design and 3D Modelling - Python in developing games, 3-D modelling and animation
- Embedded Python and IOT - MicroPython, Python on Hardware, Robotics, Arduino and Raspberry Pi
- Culture and society - Diversity, health, productivity, workspace issues, privacy, community building, coding for causes
- Others - Everything else that may be of interest to the audience.
- Core Python - Language Features, Python Implementations, Extending Python and Standard Library, language internals
- Data Science, Machine Learning and AI
- Desktop Applications - Qt, GTK+, Tkinter, Gnome, KDE, Accessibility
- Scientific Computing - Python usage in scientific computing and research. GIS, Mathematics, Simulations
- Developer tools and automation - Testing, CI/CD, Containers, Orchestration, Logging and Monitoring
- Web development - Web, Apis, Microservices
- Networking and Security - Network Programming, Network Security and Encryption
Proposal Types
- Talks
Selected Proposals
Talks
1 0
4. Scaling up Data Pipelines using Apache Parquet and Dask
2 0
7. IndicNLP - An open data platform to bring Indian languages to the advancements of NLP.
3 0
12. Anomaly Detection in Cyber Security for IoT using Federated Learning
3 0
16. Lifting Up: Deep Learning for implementing anti-hunger and anti-poverty programs-- Keras Python Library
Data Science, Machine Learning and AI
17 12
1. Interpretable Machine Learning - Fairness, Accountability and Transparency in ML systems
4 0
3. Become Language Agnostic by Combining the Power of R with Python using Reticulate
2 0
6. How I scaled up Datascience tasks by using Dask and Numba as against pyspark
5 24
10. Smart Traffic Signal in India using Deep Reinforcement Learning and Computer Vision (Simulator and OpenAI-Gym ENVS included)
5 1
13. Building and training SRGANs to enhance the quality of images
6 1
15. Creating your own Dataset for Research using Python
2 12
18. Build a Deep Convolutional Generative Adversarial Networks using Pytorch
3 3
20. Hangar, PyTorch & RedisAI; missing pieces of a complete deep learning workflow
2 0
23. Wikidata - Largest Crowdsourced Open Data Knowledge Graph
3 12
27. Improving Customer retention using deep learning in banking industry
2 3
30. Natural language processing - how machines interpret
3 1
31. Interactive Visualization with Plotly Express - Complex Plots with Simple Syntax
7 0
33. Whatcha talkin' bout Willis? Topic Modelling Twitter or What people talk about when they talk about the Budget.
5 0
42. Image Recognition in python from starting using matplotlib, numpy
11 0
43. How Watson AI Assisted Chatbot Redefines the Digital World by Giving More Personalized Touch?
2 0
46. Image Captioning using Convolutional and Recurrent Neural NetworK
4 0
50. Building Conversational AI assistants with state-of-the-art in NLP
2 0
53. Segregation of Solid Waste Using Artificial Intelligence
2 0
56. Can bone X-Ray predicts your age and height probability ? find with deep neural networks.
3 0
57. Natural Language Toolkit for Indic Languages - iNLTK
1 0
58. Building Analytical Web Apps with Dash (without the knowledge of Flask / JS)
13 0
59. How to track your Machine Learning Experiments Effectively
8 0
60. Building a Neural Machine Translation System
3 0
61. Let’s make you invisible from the surveillance cameras
4 0
62. Auria Kathi - The power of Multi Model Machine Learning Pipelines
4 0
69. Towards Contextual intelligence in Natural Language Processing
11 0
70. Are we what we watch: Analyzing the effect of YouTube videos using comments on viewer’s mental health using Python
5 0
73. Coremltools : Working around with models in iOS devices
2 0
74. Federated & Encrypted Learning to protect Data Privacy in Deep Learning
1 0
75. Automate your Data Exploration with STARK (Data Mining)
5 0
78. Fooling A Neural Network Using Adversarial Attacks
1 0
79. Your Guide To Cracking Machine Learning In Python
6 0
81. Building Conversational Experiences using an Assistant
1 0
82. Semantic Segmentation of a clinical chart with Machine Learning using Python
2 0
83. Structured data pre-processing, every data scientist's nightmare - decoded
1 0
85. Video analyzing and indexing using Azure using ML
4 0
89. Adversarial Machine Learning and Using CleverHans to make your ML models robust
3 0
90. Model and Dataset versioning practices using DVC tool
2 0
94. Keeping Track of Machine Learning Experiments
2 0
96. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
2 0
98. Identifying data blocks in CSV files using Image Processing with Python
4 0
100. Earth, Environment and The Plant Ecosystem - Palaxy!
2 0
101. Software Engineering Best Practises Applied to Machine Learning Research - AllenNLP and Data Version Control
6 0
102. Intelligent Vehicles Made Simply: An Analysis to Determine The Best Captioning Models for Self-Driving Vehicles
5 0
103. Building a search engine that queries a Probabilistic Graphical Model (PGM)
5 0