AI Learning Accelerator

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Deep Learning in the Real World

Deep learning is the technology driving today's artificial intelligence revolution. Lear from the exerts how this key technology is being applied in the real world

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.

ODSC Europe 2017

ODSC Europe 2017 is a unique collection of over 70 insightful presentations on data science modeling, tools, and languages, and topics delivered by top experts in the field. Topics include deep learning, quant finance and AI for business and more.

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.

The State of Conversational AI

This exceptional talk gives a technical overview & review of current state-of-the-art deep learning & NLP tactics for chatbots and conversational interfaces

Using AI to Answer Questions

AI expert, Joel Grus on AI systems that have a deeper understanding of the world and can demonstrate understanding through questioning and answering.

Building an Object Detection Toolkit in Tensorflow

In this talk, we will discuss how state of the art object detection techniques work. Moreover, we will explore an implementation of an open source Python object detection toolkit based on TensorFlow

Interpreting Predictions from Complex Models

With large modern datasets the best accuracy is often achieved by complex models even experts struggle to interpret. Here, we present a unified framework for interpreting predictions, namely SHAP (SHapley Additive exPlanations)