With the introduction to big data and later industry 4.0, an increased focus has arisen on the usage of data in the industry. Even though industry 4.0 still rises confusion to what exactly it is and what it is not, one thing we can agree on is that it involves data.
It is now close to a year since our first blog post Why we started a machine learning consultancy which is still getting a lot of interest. So we thought it was time to do a part two, telling the story of where we are now and how much has changed.
In this blog post we will go through the different approaches to big data, define what big data is, and how to use it to create value. There are many opinions on how to handle and use big data, the reality is that many do not have a clear idea of the amount of data that are being generated in their companies.
In this blog post, we will talk about how we can use an unsupervised learning algorithm to start creating value as fast as possible.
In this blog post, we will use data from 51 sensors to predict the probability of a future breakdown on a water pump.
In this blog post you will learn what machine vision is and how a model can be trained to detect objects in images. Machine vision or computer vision is the field of making machines see and detect objects within images and videos with a certain confidence level.
When designing the architecture for an artificial neural network, there exist a variety of parameters that can be tuned. It is indeed an art in itself to find the right combination for these parameters to achieve the highest accuracy and lowest loss. In this blog post, we are testing the usage of Talos for hyperparameter optimization of a neural network.