AI Learning Accelerator

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.

Getting to Grips with the Tidyverse (R)

In this tutorial, we'll cover some of the core features of the tidyverse, such as dplyr (the workhorse of the tidyverse), string manipulation, linking directly to databases and the concept of tidy data.

DEEP LEARNING FOR DEVELOPERS

This covers concepts of Neural Networks and Deep Learning in simple terms, with minimal theory and math. Then, through code-level demos based on Apache MXNet, we're building, training and using models based on different types of networks.

MULTIVARIATE TIME SERIES FORECASTING USING STATISTICAL AND MACHINE LEARNING MODELS

This lecture discusses the formulation Vector Autoregressive (VAR) Models, one of the most important class of multivariate time series statistical models, and neural network-based techniques.

GRADIENT DESCENT, DEMYSTIFIED

Viewers will leave the talk with a better understanding of iterative optimization and a template of their own for implementing GD in Python, should they feel this would enrich their understanding.

RACIAL BIAS IN FACIAL RECOGNITION SOFTWARE

This talk will cover the basics of facial recognition and the importance of having diverse datasets when building out a model. We’ll explore racial bias in datasets using real world examples and cover a use case for developing an OpenFace model.

DATAFY ALL THE THINGS

This session empower you to curate & create your own data sets. You’ll learn how to parse unstructured text, harvest data from interesting websites and public APIs and about capturing and dealing with sensor data.

MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING FOR DETECTING FAKE NEWS

Through use cases and examples, we will discuss the different fake news detection approaches from feature extraction to model construction. We will focus on how to leverage NLP to characterize and extract discriminative features of fake news.

EFFECTIVE TRANSFER LEARNING FOR NLP

In this talk, we explore parameter and data efficient mechanisms for transfer learning on text, and show practical improvements on real-world tasks. We demo the use of Enso, a newly open-sourced library designed to simplify transfer learning.

Artificial Intelligence at the Edge - Jameson Toole - ODSC Meetup

Building Data Science Infrastructure at the City of San Diego

Find out what type of data has city has, how a city uses its data to improve the lives of its residents and more.

User Segmentation in the Real World - A Practical Guide for Data Analysts

Ruben Kogel of VSCO walks through a logical methodology in how data analysts can approach user segmentation.