Explore >> Select a destination
|
You are here 
|
mattfaus.com | ||
| | | | |
mikiobraun.wordpress.com
|
|
| | | | | One question I often come across is how to best organize cross functional data science teams where engineers and data scientists are working closely together. Here is what I've seen to work well in practice. | |
| | | | |
www.stochasticlifestyle.com
|
|
| | | | | Component-based modeling systems such as Simulink and Dymola allow for building scientific models in a way that can be composed. For example, Bob can build a model of an engine, and Alice can build a model of a drive shaft, and you can then connect the two models and have a model of a car. These kinds of tools are used all throughout industrial modeling and simulation in order to allow for "separation of concerns", allowing experts to engineer their domain and compose the final digital twins with reusable scientific modules. But what about open source? In this talk we will introduce ModelingToolkit, an open source component-based modeling framework that allows for composing pre-built models and scales to large high-fidelity digital twins. PyData is an ... READ MORE | |
| | | | |
mikiobraun.wordpress.com
|
|
| | | | | As machine learning is becoming more mainstream (well that's already long past I guess) more and more teams who are new to ML are attempting to run data science projects. One of the most common mistakes is to think that ML is "just another library" so that people are approaching a data science project like... | |
| | | | |
www.softdevtube.com
|
|
| | | Passing data through a pipeline of transformations is an alternative approach to classic Object-Oriented Programming (OOP). The LINQ methods in .NET are designed around this, but the pipeline approach can be used for so much more than manipulating collections. This presentation looks at pipeline-oriented programming and how it relates to functional programming, the open-closed principle, | ||
