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#machine learning

Minimizing machine learning risks

How to develop machine learning responsibly

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.

Looking to the future

The state of machine learning in 2018

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.

Fastest growing, most wanted

ML trends in Stack Overflow Developer Survey 2018

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.

Packt Skill Up report 2018: Results are in

Report: What is the next big thing in web development?

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?

Star Trek predicting the future vol.4?

Amazon Alexa is programmers’ new best friend

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.

Interview with Christoph Henkelmann

Machine Learning algorithms: Working with text data

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.