Making Orbital Mechanics More Accessible With Poliastro - Episode 342

November 27, 2021 Tobias Macey Comments Off on Making Orbital Mechanics More Accessible With Poliastro - Episode 342

Outer space holds a deep fascination for people of all ages, and the key principle in its exploration both near and far is orbital mechanics. Poliastro is a pure Python package for exploring and simulating orbit calculations. In this episode Juan Luis Cano Rodriguez shares the story behind the project, how you can use it to learn more about space travel, and some of the interesting projects that have used it for planning planetary and interplanetary missions.

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Declarative Deep Learning From Your Laptop To Production With Ludwig and Horovod - Episode 341

November 22, 2021 Tobias Macey Comments Off on Declarative Deep Learning From Your Laptop To Production With Ludwig and Horovod - Episode 341

Deep learning frameworks encourage you to focus on the structure of your model ahead of the data that you are working with. Ludwig is a tool that uses a data oriented approach to building and training deep learning models so that you can experiment faster based on the information that you actually have, rather than spending all of our time manipulating features to make them match your inputs. In this episode Travis Addair explains how Ludwig is designed to improve the adoption of deep learning for more companies and a wider range of users. He also explains how the Horovod framework plugs in easily to allow for scaling your training workflow from your laptop out to a massive cluster...

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Build Better Analytics And Models With A Focus On The Data Experience - Episode 340

November 22, 2021 Tobias Macey Comments Off on Build Better Analytics And Models With A Focus On The Data Experience - Episode 340

A lot of time and energy goes into data analysis and machine learning projects to address various goals. Most of the effort is focused on the technical aspects and validating the results, but how much time do you spend on considering the experience of the people who are using the outputs of these projects? In this episode Benn Stancil explores the impact that our technical focus has on the perceived value of our work, and how taking the time to consider what the desired experience will be can lead us to approach our work more holistically and increase the satisfaction of everyone involved.

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Building Conversational AI to Augment Sales Teams at Structurely - Episode 339

November 6, 2021 Tobias Macey Comments Off on Building Conversational AI to Augment Sales Teams at Structurely - Episode 339

The true power of artificial intelligence is its ability to work collaboratively with humans. Nate Joens co-founded Structurely to create a conversational AI platform that augments human sales teams to help guide potential customers through the initial steps of the funnel. In this episode he discusses the technical and social considerations that need to be combined for a seamless conversational experience and how he and his team are tackling the problem.

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Build Composable And Reusable Feature Engineering Pipelines with Feature-Engine - Episode 338

October 31, 2021 Tobias Macey Comments Off on Build Composable And Reusable Feature Engineering Pipelines with Feature-Engine - Episode 338

Every machine learning model has to start with feature engineering. This is the process of combining input variables into a more meaningful signal for the problem that you are trying to solve. Many times this process can lead to duplicating code from previous projects, or introducing technical debt in the form of poorly maintained feature pipelines. In order to make the practice more manageable Soledad Galli created the feature-engine library. In this episode she explains how it has helped her and others build reusable transformations that can be applied in a composable manner with your scikit-learn projects. She also discusses the importance of understanding the data that you are working with and the domain in which your model will...

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