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 firstname.lastname@example.org
JSFoo:VueDay 2019 sponsors:
GuidedLDA: A Python Package using Semi-Supervised Topic Modelling by Incorporating Lexical Priors
Session type: Tutorial
Topic Models have a great potential for helping users understand document corpora. This potential is impeded by their purely unsupervised nature, which often leads to topics that are neither entirely meaningful nor effective in extrinsic tasks. In this talk, I plan to explain how we wrote our own form of Latent Dirichlet Allocation (LDA) in order to guide topic models to learn topics of specific interest to a user. I will also talk about why we proposed a simple and effective solution known as Semi-Supervised Guided Topic Model (GuidedLDA), and the process of open sourcing everything on GitHub.
[0-15mins]: Introduction to Topic Modeling and an intuition to LDA (Latent Dirichlet Allocation) with some business use-cases and an intuitive easy to understand News Article Example.
[15-20mins]: What is Guided LDA we choose Guided LDA? An understanding of the problem of unsupervised regular LDA to shifting to Semi-Supervised GuidedLDA.
[20-30mins]: How does a Generic LDA work? An Overview of the working of Generic LDA using Bayesian Probability and pertinent examples.
[30-35mins]: What Happens when we seed the document? Detailed working Explanation of the GuidedLDA in terms of Bayesian Probability and relevant examples, How it benefits than using generic LDA.
[35-37mins]: Using GuidedLDA. How to use the GuidedLDA Python Package available online on GitHub. Illustrating Sample Code for demonstrative Purposes.
[37-40mins]: Conclude with GuidedLDA stats and Key Takeaways. Motivate the audience, using a small idea that can emerge from anywhere, even from a small startup in Bangalore.
[40-90mins]: Show a small application where we first clean up a publicly available dataset and perform topic-modeling using regular LDA and GuidedLDA.
Parcipants must clone/download the following jupyter-notebook : https://github.com/NThakur20/topic-modeling
Participants must bring their own laptops and should have a basic idea on how to run virtual environments and jupyter-notebooks
I am a perpetual, quick learner and keen to explore the realm of Data Analytics and Science. I am deeply excited about the times we live in and the rate at which data is being generated and being transformed as an asset. I am well versed in domains such as Natural Language Processing, Machine Learning, and Signal Processing and share a keen interest in learning interdisciplinary concepts involving Machine Learning.
- This Repository has 200+ stars on GitHub and 700+ claps on Medium.
- Medium Article: https://medium.freecodecamp.org/how-we-changed-unsupervised-lda-to-semi-supervised-guidedlda-e36a95f3a164 (Over 700+ claps)
- GitHub Repo: https://github.com/vi3k6i5/GuidedLDA (Over 200+ stars)
- LinkedIn: https://www.linkedin.com/in/nthakur20/
- Preview: https://youtu.be/F6rxyVHGSdk
- Idea Stemmed from this Research Paper: http://users.umiacs.umd.edu/~hal/docs/daume12seeded.pdf