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 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.
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.
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.
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.
Interactive data visualization in Python
We will go through python libraries that make this extra development as frictionless as possible, and produce interactive visualizations with as little code as possible.
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.
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.
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.
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.
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.
The Magic of Dimensionality Reduction
Voted one of the best talks at ODSC Europe 2017. Dimensionality reduction is one of the most crucial tools in a data scientists’ toolbox, and modern tools can yield truly magical results.
Telling Stories with Data
Data visualisation offers a brilliant way of bringing the raw numbers to life. This tutorial will introduce an audience-centred approach to visualising data.