Blockchain AI, or Future Data Systems Must Be Built Differently
Engineering For Data Science
This talk will discuss Docker as a tool for the data scientist.
Introduction to Machine Learning
This talk gives a general introduction to machine learning, as well as introduces practical tools for you to apply machine learning in your research.
Enter the Matrix: Unsupervised feature learning with matrix decomposition to discover hidden knowledge in high dimensional data
Deep Learning in Real-Time
We will take a tour of different DL algorithms and applications, learn how different DL models are built, and see how to deploy DL models for real-time processing with SAS technology.
Mapping the Global Supply Chain Graph
Deploying your AI/FML investments
Applications of Deep Learning in Aerospace and Building Systems
This talk demonstrates using DBN, DAE, DRL and GAN in five different aerospace and building systems applications.
Applied Finance - The Third Culture
In this session we explore why it is important that we bridge the gap between the traditional data science cultures and applied finance.
Machine Learning for Mobile Sensing Applications
In this talk we’ll detail the kinds of sensor data available from mobile phones and other smart devices.
Deep Learning Techniques for Vision
This is an extremely hands-on course to take students from little knowledge of deep learning to comfort building vision models with Keras and TensorFlow.
Pavlov’s Sandman: Issues detecting snorers, training oneself not to snore via shock collar, war crime technicalities, and how to avoid all three
This talk is the journal of the explorations of a total novice audio analyst, seeking to correctly identify snores, and shock himself appropriately.
TRANSFER LEARNING: APPLICATIONS FOR NATURAL LANGUAGE UNDERSTANDING
This talk focuses on language related use cases for customer service, search, question answer, self-help and consumer finance. We'll also have some fun with applications of transfer learning.
Standardized Data Science: The Team Data Science Data Process - with a practical, example in Python
Crawling the internet Data Science Within a Large Engineering System
We will discuss some of the special considerations impacting a data scientist when designing solutions to improve decision-making deep within software infrastructure.
How to use Satellite Imagery to be a Machine Learning Mantis Shrimp
In this session we are going to start by showing you how satellite imagery actually allows you to “see” in more bands of color than the mantis (how about 26 bands) – each band is a massive amount of data about the earth.
THE PAST, PRESENT, AND FUTURE OF AUTOMATED MACHINE LEARNING
In this talk, Randy will draw from his AutoML research experience to discuss the benefits of AutoML and highlight some promising future directions of the field.
Introduction to Quant Finance with Quantiacs Toolbox
THE ADOPTION OF AI IN BUSINESS: OPPORTUNITIES AND CHALLENGES
MIT Sloan Management Review’s recent research on AI and business strategy offers a "state of the state" of AI adoption inside corporations. This session will provide an overview of organizational readiness for and adoption of AI across sectors.
PROJECT FEELS: DEEP TEXT MODELS FOR PREDICTING THE EMOTIONAL RESONANCE OF NEW YORK TIMES ARTICLES
Topics discussed will be active learning, deep learning, Bayesian inference and causality.
Bringing Your Deep Learning Algorithms to Life: From Experiments to Production Use
We will learn how to take Machine Learning and Deep Learning programs into a production use case and manage the full production lifecycle.
FROM NUMBERS TO NARRATIVE: DATA STORYTELLING
Session will cover: The essential elements of a good data story, Chart design and why it matters, Common chart design errors, and The Gestalt principals of visual perception and how they can be used to tell better stories with data.
Building an Image Search Service from Scratch
We are bringing a workshop on how you would go about building your representations, both for image and text data.
DATAOPS: ENTERPRISE DATA THAT DOESN’T SUCK
During his talk, Andy will highlights the converging factors that allow non-data native companies transform their data engineering organizations to catch up with data-native companies like Facebook, Google and Amazon.