This post will be mostly Python code with implementation and examples of the Logistic Regression theory we have been discussing in the last few posts. Examples include fitting to 2 feature data using an arbitrary order multinomial model and a simple 2 class image classification problem using the MNIST digits data.
Every time a new generation of CPUs is announced, I see a number of people writing about how they think it will be faster (or slower) than current technology because of the advertised specifications. CPU specs alone don’t tell the whole story, though, and comparing core count and clock speed across different brands or generations of processors is extremely misleading. Stop doing it!
Having issues with updating to Windows Update 1703? Are you on a Gigabyte X99 Motherboard? Try this workaround.
Due to the extraordinary amount of time and effort that goes into one of our Recommended Systems we will no longer be able to maintain and update our Recommended System for Imaris.
In this post I’ll discuss evaluating the “goodness of fit” for a Logistic Regression model and do an implementation of the formulas in Python using numpy. We’ll look at an example to check the validity of the code.
I got in line at the Starbucks’ drive-thru yesterday for my iced caramel macciato. While waiting, I noticed a familiar scene play out. This is a scene I’ve watched dozens of times since Tesla placed a Supercharger station in the Starbuck’s parking lot:
Tesla owners chatting with each other.
An analysis of NVIDIA GeForce GPU (video card) performance in Autodesk ReMake, including a look at graphics memory usage and dual GPUs.
An analysis of NVIDIA Quadro GPU (video card) performance in Autodesk ReMake, including a look at graphics memory usage and dual GPUs.
We test a lot of software here at Puget Systems, and in most cases what we are looking for is what hardware lets a given program run the fastest – or in some cases, what is the most cost effective. If you can get 95% of the best possible performance for half the price that it would cost to get a full 100%, for example, that is often a compelling way to go. However, ANSYS Mechanical (and FLUENT) present a different challenge: how can you get the best performance within the limitations of the ANSYS licensing model?
In this post I will look at “Regularization” in order to address an important problem that is common with implementations, namely over-fitting. We’ll go through for logistic regression and linear regression. After getting the equations for regularization worked out we’ll look at an example in Python showing how this can be used for a badly over-fit linear regression model.
Puget Systems builds custom workstations, servers and storage solutions tailored for your work.
We provide:
Extensive performance testing
making you more productive and giving better value for your money
Reliable computers
with fewer crashes means more time working & less time waiting
Support that understands
your complex workflows and can get you back up & running ASAP
A proven track record
as shown by our case studies and customer testimonials
