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.

Deep learning has its base in neural networks. Neural networks are (you guessed it!) networks of neurons. Vertical groups of neurons are called a layer. Each neuron accepts inputs, does some calculations and spits out an output that is sent to all the neurons in the next layer. Today we look into forward and backward propagation in neural networks (aka how a neural network learns).

Previously, we talked about bias and variance. And we learned that high variance means overfitting. But what can we do to deal with overfitting once it happens? A go-to technique is to use regularization. Let's look into it in this article.

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!

Deep learning sounds so cool, doesn’t it? There is machine learning, which is already super cool and now make it deep. Even better. But what makes a learning algorithm deep?

In this episode, I talk to AWS data scientist Naz Levent. Naz has been with Amazon for more than two years and has been part of some exciting projects in fashion, energy and entertainment industries. She shares with us how she got into data science, how she got her first job, what her days look like and why she loves her job. She has some unusual and great advice on how to get ready for a data science career.

One of my student’s on the Hands-on Data Science course asked me an intriguing question this week. After completing the course, he reminded me of the project goal we formulated at the beginning of the course and wanted to know how the model we built helped solved this problem that we defined. And the answer to his questions was: it doesn't. But with good reason.

Have you heard about the recent groundbreaking achievements in NLP? Wow, you have to read this paper... Everyone is talking about this woman/man in AI. If reading headlines like these stress you out, you're not alone.