In this video, Armando Ferreira breaks down all of the tools he uses on a day-to-day basis at his desk including his Puget Systems workstation for his 4K video editing.


In this video, Armando Ferreira breaks down all of the tools he uses on a day-to-day basis at his desk including his Puget Systems workstation for his 4K video editing.

Puget Systems helped Spot MPG move from Mac to PC for their post-production / video editing needs.

AMD is releasing a whole spectrum of new CPUs this year, from the consumer oriented Ryzen to the server-class Epyc. In response, Intel has accelerated their normal processor release cadence and is putting out new products across the board as well. We are here to explain a bit about what is going on, what to look forward to, and whether it is worth waiting for.
In this post I’ll be working up, analyzing, visualizing, and doing Gradient Descent for Linear Regression. It’s a Jupyter notebook with all the code for plots and functions in Python available on my github account.
While I am waiting on ANSYS to run some benchmarks, I wanted to take a moment to write about an event happening later this year. On a warm, summer day the air will grow chill, animals will go crazy, the sun and moon will darken, and stars will be visible even in the middle of
In Part 3 of this series on Linear Regression I will go into more detail about the Model and Cost function. Including several graphs that will hopefully give insight into the their nature and serve as a reference for developing algorithms in the next post.

Let’s get the most out of that speedy new SSD in your Puget Systems computer. Optimizing settings and configuration of the SSD can provide improved performance and reliability.

In this article we look at NVIDIA Quadro GPU (video card) options that you might consider in a workstation to see how they compare in Revit 2017.2.

In this article we look at a wide range of Intel CPU (processor) options that you might consider in a workstation, to see how they compare in Revit 2017.2.
In Part 2 of this series on Linear Regression I will pull a data-set of house sale prices and “features” from Kaggle and explore the data in a Jupyter notebook with pandas and seaborn. We will extract a good subset of data to use for our example analysis of the linear regression algorithms.