##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Introduction to Probabilistic Programming - PyMC3 and Edward
Probabilistic programming differ from deterministic ones by allowing language primitives to be stochastic. In other words, instead of being restricted to deterministic assignments such as:
rent = 25000
one can specify a probability distribution from which this house with such a rent was drawn
rent ~ Normal(mu=25000, sigma=1000)
The expressiveness of the probabilistic programming frawework, both theoretical and practical, allows us to go further into replacing parameters of Machine Learning algorithms with distributions. How do we do that?
With enhancing concerns about trust in blackbox AI, cases of small data, why will probabilistic programming help?
PyMC, Edward, Tensorflow Probability, Where do I start?
I’m so used to blackbox ML, How do I wear a Bayesian hat?
This talk tries to answer these questions.
Technically, talk will help get started with coding in PyMC3 and Edward, understand their strengths and weakness. Starting from Bayesian Inference to applying the same concepts on ML. In that sense, get an overall idea of how and where probabilistic programming helps. Code and graphs can be shown via Jupyter Notebook.
Basic understanding of widely used Probability Distributions like Normal, Poisson, Binomial. Basic understanding of Machine Learning, Neural Networks. Also Python.
It would be easier if you have Jupyter, Pymc3 and Edward installed apart from usual suspects like numpy/pandas/seaborn etc.
You might want to install a Tensorflow version < 1.7 for Edward compatibility.
Following are the pip packages I have installed for this session:
I’m Hariharan and I’m usually curious and love learning new things. I graduated from BITS – Pilani and since been in the industry for roughly 7 years. I have predominantly worked in the field of Machine Learning in my time in the industry. I love watching football, cricket. I used to love playing them, not anymore 😊. I like quizzing, despite not being good at it.