Artificial intelligence (AI) is very quickly expanding in the healthcare industry. AI’s primary role has been to automate processes to make it easier for employees to work on more complex people-related concerns. Yet as the technology develops, Healthcare Central reports that AI is also being used as a diagnostic tool. With a faster, more accurate way of detecting illnesses, and administering treatments, the mortality rate could be significantly reduced. This includes the treatment of conditions like cancer.
Since Stratio’s creation in 2014, we have posted a total of 86 posts on our blog. We would like to congratulate and thank all those Stratians who have written their posts and taught us about their specialities and discoveries in relation to Spark, Machine Learning, Deep Learning, Scala, business, Kafka… We know that is hard to find time to read all of the blog posts, so here you have a recap of the 3 most-read posts published on our blog!
Welcome back to our series on Swarm Intelligence Metaheuristics for Optimization! In part 1, we talked about a family of metaheuristic algorithms known generically as Ant Colony Optimization (ACO), which were specially well-suited for combinatorial optimization problems, i.e. finding the best combination of values of many categorical variables. Recall we define Metaheuristics as a class of optimization algorithms which turn out to be very useful when the function being optimized is non-differentiable or does not have an analytical expression at all.
“A chain is only as strong as its weakest link” – English proverb
There is one striking element that does not seem to have been addressed as a common purpose between business teams and IT teams when confronting Innovation or Digital Transformation roadmaps… A Data Management Strategy.
In the previous post about Apache Ignite, we learnt how to set up and create either a simple cache or a sql cache, and share the cached data between different nodes. In this post, we will dig a little deeper. We will see what to do if our app crashes because the cached data has disappeared. How could Ignite help us avoid this problem?
Madrid, 21 September 2018 – Today Stratio has announced the closing of a €13m Series B round led by Adara Ventures with the participation of new investor GP Bullhound. The funds raised will go toward the continued evolution of the Stratio Big Data product suite while accelerating the growth of their software subscription business internationally.
Did you know that the word “hippopotamus” is a word of Greek origin? Hippos- comes from “horse” and -potamos means “river”. The funny thing here would be to imagine when Greeks run into this animal for the very first time. There was not a word for every single animal around the world, so they probably thought something like “what a strange horse…!!! Maybe the river has something to do with it. Got it! It will be a hippo-potamus!”
This is the second post of our Wild Data series. In this post, we are going to expose how to transfer style from one image to another. Here, the most interesting point is to know that we won’t use a neural network to classify a set of classes as usual. I mean, we don’t need to train a network for a specific approach. Transfer style is based on pre-trained networks such as it could be a VGG19 trained with ImageNet (one million of images). Thus, a good understanding of transfer style will help you to better understand how convolutional neural networks works for vision. Let’s go on!
Let’s imagine that you want to buy a new car, and you fall in love with this new car’s brand. Because you really want that car, the car’s brand comes out everywhere in your daily life, even though the amount of these cars remain the same. Our brain is trained to focus on what it wants to see.
