Machine learning inevitably adds black boxes to automated systems and there is clearly an ethical debate about the acceptability of appropriating ML for a number of uses. The risks can be mitigated with five straightforward principles.
The future of digital technology is here. 2017 saw incredible progression for things like data science, artificial intelligence, and machine learning. Where will they go in 2018? In this article, Maria Thomas explores the future of data science and how well it can be combined with predictive analysis.
Every year, Stack Overflow surveys the state of the developer community. What trends, tools, and technologies did they find? Julia Silge, a data scientist at Stack Overflow, dives deep into the data to show the most loved technologies of 2018.
It’s that time of year again! The 2018 Packt Skill Up Report is out and loud! With 8000 respondents and 6 focus tracks, it gives a detailed picture of the tools and trends that define how software developers work today. In this article, we take a closer look at web development: What technologies are currently number one and what does the future hold?
Wherever you are, you can livestream the opening keynote for the Machine Learning Conference and join Xander Steenbrugge as he discusses “Cracking open the black box of neural networks”.
An in-memory computing platform with continuous learning capabilities enables a range of real-time decision making use cases. What might some of these cases be and how will they affect the future of machine learning and deep learning?
Looking to deploy and monitor large-scale deep learning applications for the enterprise? Polyaxon makes it easier to manage workloads for teams without losing control of your data.
Can’t keep your ML models straight? The new open source platform MLflow has you covered for the entire machine learning life cycle with their Tracking APIs, Projects, and Models.
Manifold may just have the solution for a problem that has been facing many ML teams. Let’s take a look at Torus: a new toolkit that promises to bring DevOps practices to machine learning. Open up the box and see what’s inside.
We spoke to data expert Canburak Tümer about how machine learning is being used to detect fraud in sales transactions. Find out how ML technology is helping to keep this tricky job under control and what it looks for when crunching the data.
With all the hype around machine learning, there’s plenty of people asking what it is exactly. If you want a quick primer on what’s important, read this.
Amazon Alexa sets a course to boldly go where no programmer has gone before! A team of scientists at the University of British Columbia led a fascinating project to task the virtual assistant with performing all the mundane programming tasks, speeding up workflow and increasing efficiency and productivity.
Deep down ML is a pure numbers game. With very few exceptions, the actual input to an ML Model is always a collection of float values. We talked with Christoph Henkelmann about the way ML algorithms work on words and letters, the difference between image and text and how to handle textual input properly.
What is the driver behind the growing interest in using Kubernetes for data science and machine learning applications? Terry Shea of Kublr explains why you shouldn’t avoid Kubernetes if you have ML and data science projects.