Being a data scientist may sound like a simple job — prepare data, train a model, and deploy it in production. However, the reality is far from easy. The job is more like taking care of a baby — a never-ending cycle of monitoring and making sure everything is fine. …

Getting an ML model into production is a hard nut to crack. According to Chris Chapo, SVP of Data and Analytics at Gap, 87% of the models fail before getting deployed. Even if you are in the lucky 13%, this is when the real hard work begins. The cycle of…

Reliance on Machine Learning models is growing. Business decisions in banking, insurance, or real-estate organizations are driven by intelligence systems. Automatization brings increased profit but comes with a great risk of failure. …

A simple guide to an understanding of different normalization methods in Deep Learning. — Training Deep Neural Networks is a challenging quest. Over the years, researchers have come up with different methods to accelerate and stabilize the learning process. Normalization is one technique that proved to be very effective in doing this.

An intuitive and straightforward tutorial about the three most popular weight initialization methods helps you pick the right one for your project. — Introduction Weight initialization is a model design parameter that the wrong choice might slow down or stall the convergence. You can think of it as a starting point for the loss function landscape. An intuitive guess would be to begin with, 0, but it also leads to 0 gradients, hence no…

The optimizer is a crucial element in the learning process of the ML model. PyTorch itself has 13 optimizers, making it challenging and overwhelming to pick the right one for the problem. In this tutorial, I will go through the five most popular optimizers explaining their strengths and limits along…

In this tutorial I will explain where and why to use a particular loss function in machine learning. After reading this blog, you will know the pros and cons of each function and which one to pick for your problem. Introduction In Machine Learning, the loss function defines how far is…