NIVIDA announced availability of the the Titan V card Friday December 8th. We had a couple in hand for testing on Monday December 11th, nice! I ran through many of the machine learning and simulation testing problems that I have done on Titan cards in the past. Results are not the near doubling in performance of past generations… but read on.
The new Intel core-i9 and core-i7 “enthusiast” “X”, Skylake-X processors and the single socket Xeon Skylake-W (Workstation) processors seem nearly identical. I’ll discuss the differences and make my recommendation on which to use.
Intel Purley platform, Skylake-SP, Xeon “Scalable” processors (Platinum, Gold, Sliver, Bronze) are here. All 58 of them! Hopefully this post will help you to decide which of these (excellent) processors may be of use for your applications. I trim the list do to just a few of my favorites and break them down by use-case.
ARM for HPC? Supercomputers using ARM processors? Yes! I was at SC17 last week and ARM was a hot topic. There are new ARM processor designs that are fully competitive with Intel and AMD CPU’s for high performance computing.
Which Intel CPU is for heavy numerical compute workloads, Skylake-X core i7 7800X or Coffee-Lake core i7 8700K? They are priced nearly the same. The 8700K has high core clock frequencies and good power management but the 7800X has AVX-512. I show you which one comes out on top using an Intel optimized Linpack benchmark.
Intel Core-i9 7900X and 7980XE are very good desktop processors for mathematical computing workloads. This post is a short listing of results for the Linpack benchmark which is still my personal favorite CPU performance metric.
I can’t think of of trending field of scientific research that has ever been better suited for “beginners” than Machine Learning and AI. Even though the field has been around for decades it feels like day one. There is now a perfect convergence of resources to facilitate the learning and doing of Machine Learning.
The post will implement Multinomial Logistic Regression. The multiclass approach used will be one-vs-rest. The Jupyter notebook contains a full collection of Python functions for the implementation. An example problem done showing image classification using the MNIST digits dataset.
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.
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.
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