The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:
- Model management, including data cleaning, instrumentation and productionizing data science.
- Bad data and case studies of failure in building data products.
- Identifying and handling fraud + data security at scale
- Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
- Feature engineering and ML platforms.
- What it takes to create data-driven cultures in organizations of different scales.
1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.
Why you should attend?
- Network with peers and practitioners from the data ecosystem
- Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
- Demo your ideas in the demo session
- Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.
Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email email@example.com
JSFoo:VueDay 2019 sponsors:
Unpacking the Learning Paradigms
Session type: Birds of a Feather session of 1 hour
Struggling to unpack the plethora of learning paradigms in ML? Let us have a dialogue to both understand them better and build a better mental model to explain them to everyone.
It starts all simple. You predict the price in Boston housing data and understand that this is Supervised Learning. You reduce the dimensions in the Iris flower data and understand that this is Unsupervised Learning. Then you want to know how the Chess & Go data was used and understand there is Reinforcement Learning.
You move to time-series data and now you have something like Auto-Regressive learning. Or dabble in text data, and try to get your head around all these word vectors and language models. Soon you are reading about an alphabet soup of suffix-paradigm-Learning – Semi-Supervised, Self-Supervised, Weak-Supervised … and now you are struggling to make sense of it all. Throw in a bit of statistical model literature: Generative Learning vs. Discriminative Learning, Frequentist Learning vs. Bayesian Learning and it no longer looks simple anymore.
This BoF session is to have an open dialogue on the Learning Paradigms and start to unpack them to build a better mental model around them. Some of questions we would be keen to unpack are:
- Why is it important to have a mental model around the learning paradigms?
- How to think about learning from data as a spectrum of algorithmic (and model) techniques, rather than neat categorisation of learning buckets?
- What are some useful analogies and constructs that can help both describe and explain these in a way that builds intuition and cognition?
- How to successfully navigate the surge of new techniques, models and tricks that keep emerging in ML papers and libraries?
- How can we develop a better vocabulary for Machine Learning to be better able to explain to the business and general audience what is really happening?
If you have been doing Machine Learning for a while, and have been struggling with a mental model to understand and explain the Machine Learning paradigms, then this is a great session to come participate and share your challenges and perspective on this.
However, if you have just started your learning journey in Machine Learning, then it is possible the session would leave you with more questions than answers. But then again it may serve as good initial weights for your own learning model!