Getting Creative with Generative Adversarial Networks
Neural networks can simplify our lives, give them a boost and help us challenge ourselves. They can judge your outfit and send pick up lines to your matches on Tinder. The experimental phase of neural networks is not over yet. Generative Adversarial Networks (GANs) allow you to see what you would look like 20 years in the future, let Donald Trump say anything you want, or create your novel design chair.
ML2Grow’s recipe for business-wise AI success stories
AI is cool. AI is hot. AI can make computers win Jeopardy, play Pong at lightspeed (that’s even quite simple) and allows robots to make backwards salto’s. That’s fun. That’s impressive. But what’s the business value for you? How do we use the results of this magnificent research for real solutions for your company? Solutions that actually grow your business?
Getting Nostalgic with Recurrent Neural Networks
A Recurrent Neural Network (RNN) is a 20+ year old concept that is becoming relevant again due to advances in Deep Learning. Typical examples are speech recognition devices such as Alexa, Siri and Google home that respond to our questions and can perform some simple tasks. It can be found in google translations or chatbots. Despite the booming rise of RNN’s, many struggle to get intuition with it. This blog explains the concept of recurrent neural networks, sparing the math.
Machine Learning for your Grandma
The strawberry tale We explain the very basics of ML with a story that draws parallels between human learning and machine learning. One day, Harry decides it’s time to visit his grandmother in the retirement home. To surprise her, he decides he will buy the sweet strawberries that she likes. Laura, the saleswoman, sells strawberries […]
It’s not magic, it’s data!
Countless companies are either working on machine learning projects or are dreaming of using the technology as quickly as possible. However over time, many of these ambitious projects don’t deliver the desired outcome. This is often due to the poor quality of the available data that are fed into the algorithms. “Garbage in, garbage out” is an iron law in the field of machine learning. That’s why data scientists are of crucial importance in any machine learning project. They analyze and clean the data, transform it into the desired format with the required quality
It’s training cats and dogs
Image recognition is one of the biggest success stories of machine learning technology and can be found everywhere in our lives, used daily by thousands of companies and millions of consumers. Image recognition is driven by deep learning and more exactly by Convolutional Neural Networks (CNN), a specific neural network architecture. CNNs have been successfully applied to identify faces, objects, road signs, to implement filters on your smartphone. It gives vision to robotics applications and fuels the self-driving car. CNN applications are not limited to these cases. They also allow computers to distinguish cats from dogs. Let’s dive right in and have a look under the CNN hood!
Serving GPflow models
Deploying models as webservices has become straightforward using TensorFlow: exporting the graph as SavedModel allows easy deployment with Tensorflow Serve or the Cloud ML engine. But how can you obtain a SavedModel when using the GPflow library for Gaussian Processes?
Recommendations systems are not new. In 2006, Netflix started a one million dollar competition to improve their recommendation algorithms. In 2019, 75% of their viewed content is attributed to personalized recommendations.
Learn about how this technology allows you to personalize the interactions between your customers via dedicated newsletters, promotions or your website.
ML2Grow’s secret recipe for customer churn prediction
Customers churn when they stop purchasing your goods or services. While usually no expense is spared to find and attract new customers, the same seldom applies to identifying customers which are ready to join your competitor. But how exactly can you focus your efforts to the right customer, find out in this article!
It’s a match! ML2Grow welcomes three new colleagues
We were able to welcome no less than three new colleagues in October! Anton arrived as software engineer after he did his master’s thesis with Joeri. Julie and Mike are completely new and come to complete our team as data scientists.