It is important to have a good understanding of possible approaches to hyperparameter tuning to be able to efficiently make the correct decisions when it comes to tuning your network. Let’s take a quick look into why this is an issue, to begin with, and review the current techniques out there that you can use on your projects.

If you ever ventured into the world of Deep Learning, you might have gotten stuck where many other people do: Which technology do I use? We have quite a few options of deep learning APIs but not all are suitable for first-time users. Let’s look into them one by one and decide on which one to use.

Bias and variance are some of the trickiest concepts to get a solid understanding of. It was explained to me a bunch of times and every single time after a couple of weeks I found myself thinking “Which one was which again?” So today, I’m on a mission to explain it to you in a practical way in hopes that this might be the last time you might need someone to explain it to you!

By now you have probably heard this explanation: ML algorithms learn like humans. You give it examples and it recognizes and remembers the patterns in them. You need to give it a lot of examples, though, so that it can learn accurately. Okay, that’s clear. But HOW does it learn?

It is pretty much a stereotype that data scientists can’t write clean and understandable code. This doesn't have to be the case for you. By learning a few principles of how to write code properly, you can use the stereotype to your advantage and set yourself apart from the competition.

When changing careers into data science, if you have years of work experience, it's only fair to expect a higher position than junior data scientist. In order to achieve it, you need to approach your job hunt differently than newly grads. Here are some tips for signalling to your future employer that you will bring value.