Anthill Inside 2019

A conference on AI and Deep Learning

Make a submission

Accepting submissions till 01 Nov 2019, 04:20 PM

Taj M G Road, Bangalore, Bangalore

About the 2019 edition:

The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule

The conference has three tracks:

  1. Talks in the main conference hall track
  2. Poster sessions featuring novel ideas and projects in the poster session track
  3. 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:

  1. Data scientists
  2. AI, DL and ML engineers
  3. Cloud providers
  4. Companies which make tooling for AI, ML and Deep Learning
  5. 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 sales@hasgeek.com


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Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.


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Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more

Hariharan C

@harc

Introduction to Probabilistic Programming - PyMC3 and Edward

Submitted Apr 13, 2019

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.

Outline

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.

Requirements

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:

absl-py==0.7.1
astor==0.7.1
backports-abc==0.5
backports.functools-lru-cache==1.5
backports.shutil-get-terminal-size==1.0.0
backports.weakref==1.0.post1
bleach==1.5.0
cycler==0.10.0
decorator==4.4.0
edward==1.3.5
enum34==1.1.6
funcsigs==1.0.2
futures==3.2.0
gast==0.2.2
grpcio==1.20.1
h5py==2.9.0
html5lib==0.9999999
ipykernel==4.10.0
ipython==5.8.0
ipython-genutils==0.2.0
joblib==0.12.5
jupyter-client==5.2.4
jupyter-core==4.4.0
Keras-Applications==1.0.7
Keras-Preprocessing==1.0.9
kiwisolver==1.1.0
Markdown==3.1
matplotlib==2.2.4
mock==3.0.5
numpy==1.16.3
pandas==0.24.2
pathlib2==2.3.3
patsy==0.5.1
pexpect==4.7.0
pickleshare==0.7.5
prompt-toolkit==1.0.16
protobuf==3.7.1
ptyprocess==0.6.0
Pygments==2.4.0
pymc3==3.6
pyparsing==2.4.0
python-dateutil==2.8.0
pytz==2019.1
pyzmq==18.0.1
scandir==1.10.0
scipy==1.2.1
seaborn==0.9.0
simplegeneric==0.8.1
singledispatch==3.4.0.3
six==1.12.0
subprocess32==3.5.3
tensorboard==1.6.0
tensorflow==1.6.0
tensorflow-estimator==1.13.0
termcolor==1.1.0
Theano==1.0.4
tornado==5.1.1
tqdm==4.32.1
traitlets==4.3.2
wcwidth==0.1.7
Werkzeug==0.15.4

Speaker bio

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.

Links

Slides

https://www.slideshare.net/hariharanchandrasekaran9/into-to-probproghari-2

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Make a submission

Accepting submissions till 01 Nov 2019, 04:20 PM

Taj M G Road, Bangalore, Bangalore

Hosted by

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more