20–23 April 2020

Interactive sessions

New: Interactive sessions (powered by Katacoda) give you the chance to manipulate technology in real time to discover how it works. You’ll input, edit, run code and render live results as you learn with guided instruction.

Wednesday, April 22

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11:1512:45
Location: S11 A
Max Humber (General Assembly)
Max Humber helps you get your model in front of users as quickly as possible. You'll discover a step-by-step lean ML playbook showing you how to convert your idea into a fully deployed application. Read more.
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14:0515:35
Location: S11 A
Thomas Nield (Nield Consulting Group)
Linear regression, logistic regression, and Naïve Bayes are workhorse machine learning algorithms that achieve practical results with little overhead. As a matter of fact, building these algorithms from scratch (without libraries) is more accessible than you may think! Read more.
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14:0515:35
Location: S11 B
Axel Sirota (ASAPP)
Over this training, we will learn in a hands-on approach about Tensorflow Lite and how to leverage it to create a machine learning application that can run on your cell phone. Read more.
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16:3518:05
Location: S11 A
Sarah Guido (InVision)
Getting your data ready for modeling is the essential first step in the machine learning process. Sarah Guido outlines the basics of preparing and standardizing data for use in machine learning models. Read more.
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16:3518:05
Location: S11 B
Aileen Nielsen (Skillman Consulting)
This talk poses the question of whether deep learning will ever come to dominate time series forecasting as it has come to dominate approaches to language and imagery. We'll both ask the question and provide a partial answer. Read more.

Thursday, April 23

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11:1512:45
Location: S11 A
Jeff Carpenter (DataStax)
In this hands-on training, you’ll learn how to incorporate Apache Cassandra and Apache Kafka into your data pipelines, using the Kafka Connect framework and the DataStax Kafka source and sink Connectors. Read more.
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14:0515:35
Location: S11 A
Joseph Nelson (Roboflow)
In this session, Joseph walks you through the end-to-end flow required to train a model for mobile deployment, including image collection, preprocessing and augmenting considerations, model training, and saving the TFLite model in an appropropriate format for deployment. For this session, participants should have awareness of machine learning, familiarity with Python, and their laptops. Read more.

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