Everybody using Python would nowadays be using Anaconda instead of just plain old python installed. And if you aren’t, my recommendation is to use it. Why may you ask? Because with python comes a lot of packages and each such package comes with a host load of dependencies and it is difficult & time-consuming to resolve those dependencies manually. Also, anaconda allows one to create multiple environments (basically environment containers for package isolation). So let’s say you need package1 for some type of work and package2 for some other type of work. But package2 inherently depends on a different version of package1. Now if you have the same environment, you will have conflicts but creating different isolated environment containers helps the use case.

There are many different solutions to remote control your Linux workstation but most of them are not effective. For example, one can always ssh into the workstation and forward X. Well, this kind of works but has several drawbacks; the major one being that all the processes you started with your ssh session will be killed when you loose your SSH session. I call nohup, screen, etc. as bypass methods because they essentially try to give you a workaround solution instead of actually solving it. And if you are like me who keeps logging in and out and starting/stopping scripts, I am pretty sure you will forget to use those just like me and login after a good night sleep to find out that your compile script got terminated because you forgot to screen it! Also, SSH with X forwarding typically needs an X-Server on your host machine and can be a PIA to setup if you are on Windows. Yes, yes, we can all use MobaXTerm but hey, believe me, X-server is heavy in terms of resource usage on Windows. Also automatic file changes are not detected with that solution.

I know you are interested in #MachineLearning and your first instinct is to use #TensorFlow (of course since it is backed by #Google) and you will probably find a lot of support with queries. The best part is that the available #Docker container will help you experiment and it simply runs out of the box. Look at my article on getting started with Tensorflow. Soon you will get bored by the amazingly slow executing speeds on CPU and will be thirsty to run it on your GPU because:

We all have Twitter accounts and we all tweet stuff. But sometimes, we all suffer from information overload. And we all need to get more followers right? So one of the things we can do is automate Twitter posts using a tweet bot. I am using nodejs as my framework (simply because it works for webapps pretty good). You can search on the web to understand how to install nodejs and npm. Use the latest version and it should work fine. I am also using a Twitter API client for nodejs known as Twit. Twit is a very simple library that wraps all the twitter APIs in easy to use function calls. The code is pretty straight forward as can be seen below.

Meteor is a subscribe publish based application development framework which allows for rapid application development using the same code base. I had the chance of working on one of the most demanding meteor applications ever written. The client is a high profile company working with automation. So basically one of the problems they faced was to simulate tens of thousands of devices in a virtual environment. One of the most logical ways to go would be to dockerize the client and then use docker swarm mode to auto scale the clients. But the code base right now is a monolith and hence dividing the different functionality into micro-services is doable but will take a long time. The client wanted this done like the day before I got my hands dirty.

Machine learning (ML) is the “FUTURE”. I have been reading about it for quite some time now and I am pretty convinced by the statement. We are all talking about BigData, predictive analytics, etc. but really, when a system dude like me tries to foray into the field of ML, everything seems so overwhelming. The discussion starts with having millions of records (if you are lucky). Otherwise, it is 4TB of unstructured data as a start. Your systems brain tries to grasp the big picture and gets lost in trying to figure out the details. But well, after reading around, grappling, experimenting and reading a bit more, I think that learning ML is doable for us system dudes. You do not have to be a math genius (well, it helps if you are). But I will start with this blog of mine documenting the baby steps needed. I will use it as my reference and you can use it as yours if you find it useful.