Anthill Inside Miniconf – Pune

Machine Learning, Deep Learning and Artificial Intelligence: concepts, applications and tools.

##About the event

When it comes to Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI), three aspects are crucial:

  • Clarity of fundamental concepts.
  • Insights and nuances when applying concepts to solve real-world problems.
  • Knowledge of tools for automating ML and DL.

Anthill Inside Miniconf will provide understanding on each of these fronts.


This miniconf is a full day event consisting of:

  1. 3-4 talks each, on concepts, applications and tools.
  2. Birds of Feather (BOF) sessions on focussed topics.

We are accepting proposals for:

  • 10 to 40-minute talks, explaining fundamnetal concepts in math, statistics and data science.
  • 20 to 40-minute talks on case studies and lessons learned when applyng ML, DL and AI concepts in different domains / to solve diverse data-related problems.
  • 10 to 20-minute talks on tools on ML and DL.
  • Birds of a Feather (BOF) sessions on failure stories in ML, to what problems / use cases should you use ML and DL, chatbots.
  • 3-6 hour hands-on workshops on concepts and tools.

##Hands-on workshops

Hands-on workshops for 30-40 participants on 25 November will help in internalizing concepts, and practical aspects of working with tools.
Workshops will be announced shortly. Workshop tickets have to be purchased separately.

##Target audience, and why you should attend this event

  1. ML engineers who want to learn about concepts in maths, stats and strengthen foundations.
  2. ML engineers wanting to learn from experiences and insights of others.
  3. Senior architects and decision-makers who want to quick run-through of concepts, implementation case studies, and overview of tools.
  4. Masters and doctoral candidates who want to bridge the gap between academia and practice.

##Selection process

Proposals will be shortlisted and reviewed by an editorial team consisting of practitioners from the community. Make sure your abstract contains the following information:

  1. Key insights you will present, or takeaways for the audience.
  2. Overall flow of the content.

You must submit links to videos of talks you have delivered in the past, or record and upload a two-min self-recorded video explaining what your talk is about, and why is it relevant for this event.

Also consider submitting links to the following along with your proposal:

  1. A detailed outline, or
  2. Mindmap, explaining the structure of the talk, or
  3. Draft slides.

##Honorarium for selected speakers; travel grants

Selected speakers and workshop instructors will receive an honorarium of Rs. 3,000 each, at the end of their talk. We do not provide free passes for speakers’ colleagues and spouses.

Travel grants are available for domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans.
If you require a grant, mention this in the field where you add your location. Anthill Inside Miniconf is funded through ticket purchases and sponsorships; travel grant budgets vary.

##Important dates

Anthill Inside Miniconf – 24 November, 2017.
Hands-on workshops – 25 November, 2017.

##Contact details:
For more information about speaking, Anthill Inside, sponsorships, tickets, or any other information contact or call 7676332020.

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

Sameer Mahajan


Leapfrog in Deep Learning

Submitted Nov 3, 2017

Machine learning (ML) gives computers the ability to learn without being explicitly programmed. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, ML explores the study and construction of algorithms that can learn from and make predictions on data through building a model from sample inputs. It’s a really exciting & impactful phase in the ML journey. Today, every time you go to a website, most likely there’s a ML algorithm behind the scenes, analysing the data and interactions, radically heightening your experience using ML

This fast paced hands on worskhop is designed to bootstrap your Deep Learning. It quickly on boards Machine Learning concepts like regression, classification,matrix factorization etc. It introduces algorithms like k Nearest Neighbors, k means, recommender systems etc. It brings in tools like python for quick coding,pandas and numpy for data munging, matplotlib for visualization, scikit-learn for ready made machine learning algorithms. It does so with real life use cases like predicting house sale prices, sentiment analysis using restaurant reviews; real life data like people wikipedia, adult income data etc. and lots of hands on coding. We dive into intuition behind commonly popular algorithm of gradient descent, forward and backward propagation in neural networks. This approach helps imbibe the concepts effectively. We go onto implementing logistic regression as single layer neural network from scratch completely in python. Later we implement generic multi layer neural network in tensorflow.

You can download the entire course content (follow along slides, data for hands on assignments, developed code for all hands on assignments) from github repository of During the course you will develop all the code outlined here from scratch under the guidance of the instructor. I hope that you continue referring to programs developed here for tools, technologies and techniques (3 Ts) even as you progress through your Deep Learning career! Good Luck!


  1. Machine learning overview : 15 minutes
  2. Introduction to python, pandas, numpy, jupyter and sci-kit learn: 45 minutes
  3. Regression for predicting house prices: 30 minutes
  4. Classification for sentiment analysis: 30 minutes
  5. Clustering and introduction to unsupervised learning: 30 minutes
  6. Recommenders: 30 minutes
  7. Deep learning and neural networks: 30 minutes
  8. Tensorflow: 30 minutes
  9. Writing neural network algorithm from scratch in tensorflow: 30 minutes
  10. Next steps, closing remarks and QA: 30 minutes


• Laptop with Ubuntu / linux, charged battery + charger, docker installed.
• You can use readymade public docker image with everything installed including tensorflow from (run it as sudo docker run -it -p 8888:8888
• If people come with windows laptop we can provide opensource tools like graphlab create etc. We can publish the details upfront so that they can come prepared with all these pre installed.

Maths Knowledge Requirements
It would help if you brush up the following topics from high school. Although these are not mandatory, we will cover enough details at the time of workshop.
• Basics of derivatives and concept of maxima-minima.
• Basics of matrix and vector manipulation from linear algebra.
Programming Knowledge Requirements
o Basic programming
o Reading and Writing files
o Flow controls (if-else)
o Looping constructs like for loop, while
o Variable assignments

Speaker bio

I am a mentor for Machine Learning Foundations course in Machine Learning Specialization on coursera. I have also successfully completed Andrew Ng – Stanford’s machine learning course, the complete set of Machine Learning specialization and deep learning specialization courses on coursera. I have 22 years of experience in software industry in companies like Microsoft, Symantec etc. across US and India. I hold 8 US patents issued in my name with a few more in the pipeline. I am an alumnus of IIT Bombay and Georgia Tech CS departments. I have taught ML 101 to over 100 students in my current company GS Lab where I work as a Principal Architect.



{{ gettext('Login to leave a comment') }}

{{ gettext('Post a comment…') }}
{{ gettext('New comment') }}
{{ formTitle }}

{{ errorMsg }}

{{ gettext('No comments posted yet') }}

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