Two weeks ago we came to you with this post bemoaning the fact that there was no way we could predict who was going to die in the last season of Game of Thrones. We arrived at this conclusion using data we had on the TV show characters. But we enjoy a challenge so instead we turned to the data we had from the books. And guess what? We had a breakthrough!!!
Game of Thrones Season 7 was too short and left us wondering who is going to die in the last season... in two years 🙁. Wouldn’t it be cool to be able to speculate a little while we’re waiting??
Transaction logs are generally stored securely, but companies often don’t take advantage of this fact. A transaction can be any instantaneous exchange of goods, services, funds.
Imagine you desperately need to improve the trustworthiness of your company. Let’s say you work for Backblaze, an online data storage service, and you have batches of hard drive disk data in your fridge. Let’s not waste them, but rather let’s make a list of the 50 most at risk disks, in order that they can be carefully watched by the technicians, and if necessary, be replaced before a failure occurs. Shall we start cooking the data?
Even if DNN has huge potential for application to all kinds of problems, it also has some significant issues of its own (as with most other ML algorithms): models will fail to manage uncorrelated features; by design, models will over fit and learn from irrelevant, noisy or rare data; and so on. In this post, we propose to pre-process data to automatically reject uncorrelated features and let DNN work on only statistically relevant and optimised data.
Using an Automatic Machine Learning pre-processing algorithm designed by PredicSis, we can improve and accelerate Tensorflow modelling.
In this blog, you'll discover how AI can help Tableau users to save valuable hours finding salient information by automatically proposing relevant correlations, discretization and groupings.
Image Recognition is a complex field generally restricted to experienced data scientists with in-depth knowledge of Deep Learning. There exist many tutorials on digit recognition (MNIST) or how to classify whether an image is a dog or a cat. Such tutorials use exclusively Tensorflow, Theanos, Caffe, Keras and other similar Deep Learning frameworks. Some explain how to train the machine from scratch and some explain how to re-train the machine. In this article we will show that the same methodology can be applied without needing to use Deep Learning, but through more classic Machine Learning tools like PredicSis.ai.
Working in an AI start-up can sometimes be an uphill challenge, but hopefully less so for someone who has climbed Mount Fuji…
I recently read Erik Vermeulen’s interesting post about the different ways for start-ups to grow in a digital age and he mentions three specific considerations: building an eco-system, leveraging technology to deliver constant innovation, and adapting to the environment. In his article on hackernoon (click here for full details) Erik uses the example of an Indonesian start-up, Go-Jerk, which he says is definitely NOT a ride sharing company, or even a company in the conventional sense, despite appearances (again, I strongly suggest you read the article as I do not want to do it an injustice here).
We hear all the time that the most important element for a start-up to succeed is its strategy relating to product, value proposition, raising funds, etc. But what is a bit less put forward is that HR is the one of first things you should consider as part of your strategy! Understanding the culture of a start-up and understanding what drives its employees can ensure you make the right decisions for the future of your company and help prepare it for success.