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.
Google released the beta version of Android P, the company’s latest mobile operating system, and it’s stuffed with all kinds of smartness! Machine learning enthusiasts, gather round; it’s play time!
Humanity is confronted more than ever with artificial intelligence (AI), yet it is still challenging to find a common ground. We talked with Marisa Tschopp, researcher at scip ag about Artificial Intelligent Quotient (A-IQ), how to automate A-IQ testing and more.
If you want to know the ABCs of machine learning or if you need to brush up on basic concepts, the first part of the latest JAX Magazine issue is for you. If you’re a machine learning aficionado or expert, you’ll surely enjoy the second part.