SESSION + Live Q&A
Intuition & Use-Cases of Embeddings in NLP & Beyond
Machine Learning has achieved tremendous advancements in language tasks over the last few years (think of technologies like Google Duplex, Google Translate, Amazon Alexa). One of the fundamental concepts underpinning this progress is the concept of word embeddings (using something like the word2vec algorithm). Embeddings continue to show incredible power for representing words in a way that machines can use to do some very useful things by solving complex language problems. More recently, companies like Airbnb and Alibaba have started using the concept of embedding to empower non-NLP use-cases like recommendations, search ranking, and personalization.
In this talk, we will go over the intuition of word embeddings, how they're created, and look at examples of how these concepts can be carried over to solve problems like content discovery and search ranking in marketplaces and media-consumption services (e.g. movie/music recommendations).
Speaker
Jay Alammar
VC and Machine Learning Explainer @STVcapital
Through his blog and lessons on Udacity, Jay has helped tens of thousands of people wrap their heads around complex machine learning topics. Jay harnesses a visual, highly-intuitive presentation style to communicate concepts ranging from the most basic intros to data analysis, interactive intros...
Read moreFind Jay Alammar at:
Location
St James, 4th flr.
Track
AI/Machine Learning without a PhD
Topics
NLP/NLUMachine LearningShare
From the same track
How to Prevent Catastrophic Failure in Production ML Systems
AI systems can fail catastrophically and without warning, a characteristic not welcomed in the corporate environment. Martin will describe the unpredictable nature of artificial intelligence systems and how mastering a handful of engineering principles can mitigate the risk of failure. You’ll...
Martin Goodson
Chief Scientist/CEO @EvolutionAI
Test Driven Machine Learning
Software engineers are familiar with test driven development, but are not familiar with the statistical testing required in machine learning. Machine learning specialists are familiar with testing during the model building phase when they withhold data for cross-validation or final testing, but...
Detlef Nauck
Chief Research Scientist for Data Science @BTGroup and Visiting Professor @bournemouthuni
H2O's Driverless AI: An AI that creates AI
Through my kaggle journey to the top spot, I have noticed that many of the things I do as a data scientist can be automated. In fact automation is critical to achieve good scores and promote accountability, ensuring that common pitfalls in the modelling process are prevented. Through...
Marios Michailidis
Competitive Data Scientist @h2oai
Understanding Deep Learning
No matter what your role is, it is really important to have some understanding of the models you’re working with. In last year's keynote, Rob Harrop talked about the importance of intuition in machine learning. This is a step towards that. You might already be using neural networks. How can...
Jessica Yung
Machine Learning blogger and entrepreneur, Self-Driving Car Engineer Scholar @nvidia
AI/Machine Learning Open Space
Shane Hastie
Director of Agile Learning Programs @ICAgile