So it looks similar to AWS Systems Manager, but only for Windows and Linux in GCP. In their Youtube video at https://www.youtube.com/watch?v=LeaA66WUaaM&feature=youtu.be they're saying however you're doing "patch compliance" whether it's orchestration or by-hand it is still essentially being done incrementally by hand possibly one package at a time. So instead of using Terraform/Ansible/Vagrant to connect to GCP you can use their VM manager to perform bulk updating of OS packages. Their VM manager relies on agent software to connect directly to your VM to issue system commands via your OS native console.
An oversimplified explanation. SymPy is a non-interactive toolkit for balancing the numerical coefficients of a system of nonlinear equations. FYI a "system" in this sense is a math term. I also found that SymPy is too niche because it's not interactive.
You should ask to talk to a developer before accepting an obviously complicated migration. Ask about the numbers since they're not IP/Properitary. How many lines of code? How many functions? How many standard modules? How many custom libraries? Is everything Python? Are other languages being used too? That way you will know whether or not they are intentionally hiding the real issues from you to get you to do free work.
A bit off topic, but unit test coverage trivializes the work that goes into conducting a migration. Unit test coverage really only pertains to upgrades. A migration is not synonymous with an upgrade. Migrations are serious work that typically span entire ecosystems.
When I mentioned "the numbers" I wasn't hinting at unit test coverage. I was giving examples as to what the numbers could be. Obviously OP should ask lots of questions about all of the software running on a legacy kernel.
I remember in an interview a developer had asked me to resolve the very same problem. And, I actually at the time never heard of it. He said to me that he knew I didn't have a lot of experience using Docker.
Yikes! I'm surprised by the number of people who think temp RAM disk for QA testing came from using containers or the cloud. It was actually in use prior to that technology. And, it was popularized in the use of remote PXE installation which required modifications to the OS after the fact.
My contribution is correct. I'm not a parrot. Temp RAM disk started in Linux 2.3 (possibly older than that), it didn't come from builds inside containers or instances in the cloud.
No. You can get similar language-level benefits from languages with stronger ecosystems around web-relevant technologies. For example, F# is very close to Ocaml, but has a healthier ecosystem for web stuff.
ReScript (formerly called ReasonML), is an interesting option for web apps (especially the front-end part), if you are interested in Ocaml-like languages.
Language-wise, it is Ocaml with a different syntax layer, so all of the nice things about the language are still available.
Researchers determined that a NN of saliency maps could predict El Niño/Southern Oscillation (ENSO) in climate simulations based on relatively few observational records (page 2).
I was wondering whether the numerical representations of individual faces were PCA numerical series. Principal Component Analysis (PCA) is the most common method for image-based recognition, image preprocessing, lossless compression, signal-noise analysis, and high resolution spectrum analysis.
PCA can transform an image into a set of unique components, where each component has a numerical degree of distance and relatedness from an agreed on centered component. The first component has the largest possible variance (it accounts for most of the variability in the group). Each succeeding component has the highest variance that is orthogonal to the preceding components. The transformation of the group proceeds linearly from a group with a high degree of dimensionality to a group with a low degree of dimensionality of which the components of the group with a low degree of dimensionality are uncorrelated.
PCA reduces the dimensionality of a complex group of possibly unrelated activities into a smaller group of principal components that accurately represent the entire group with minimum information loss and no loss of essential intrinsic information. PCA also reveals the internal structure of a group of possibly unrelated activities, it can be used to discover meaningful relationships based on commonalities the internal structure of the group shares with other activities that happened in the past. PCA is well known for forcasting with time-series analysis and regression analysis. In most cases the predictability of specific activities can be calculated with high percentages of certainity, by focusing the reconstruction of projected outcomes on the optimization/maximization of the variances of specific activities.
In addition by categorizing the images into age groups and gender groups that information would be very valuable beyond marketing in longer list of industries world-wide.
i've talked to a few biotech startups, usually a ceo or founder. the main issues are a consensus in the startup that (1) it's easier to combine numerous unrelated technologies that may not even run in similar environments/platforms to solve a very specific problem, and (2) it doesn't matter their level of skill in applying the tech to solve the very specific problem.
Yeah, most real work using tech doesn't care about platform/environment. Which is why things like Unix get so much usage.
These biotech people have such a higher appreciation for turning input into valuable output then most "computer scientists" have after 20 year careers.
