Mobile developers: What are some of the ways that you can implement machine learning into an Android application? If you have been planning your very own ML-based app solution, read on to learn about a few applications of non-device machine learning. Find out some basic ML APIs and their potential uses in mobile apps.
In his talk at Machine Learning Conference 2019 in Munich, Oliver Zeigermann talked about how chess engines work. Game playing is a classic discipline of AI and had a major break through in the 90s when Deep Blue defeated Kasparov and arguably became the world’s best chess player.
Machine learning has many use cases and offers up a world of possibilities. However, some people might be put off and think it’s too difficult. It’s not. You don’t have to be a machine learning pro to use TensorFlow Lite. Here’s how to get started building your own customized machine learning model on Android.
Eric Reiss started working with user experience (UX) long before the term was even known. Over the past 40 years, he has encountered many issues that have disturbed him – from creating purposely addictive programs, sites, and apps, to the current zeitgeist for various design trends at the expense of basic usability.
The success of deep neural networks in diverse areas as image recognition and natural language processing has been outstanding in recent years. However, classical machine learning and deep learning algorithms can only propose the most probable solutions and are not able to adequately model uncertainty.
DevOps means development is moving faster than ever. How is it possible to ensure everything is secure, especially when migrating to the cloud? Ivan Novikov takes a close look at how to keep everything moving at pace without leaving gaping security gaps behind.
Every Monday, we take a step back and look at all the cool stuff that went down during the previous week. Last week we had an eye-opening interview about OpenWebStart, discussed the implications of the GitHub restrictions imposed over trade laws & more. Let’s take a look.
Artificial intelligence and machine learning are changing how marketing campaigns work for many companies. They make it possible to transform datasets that allow better decision-making. However, it is not a magic solution, and there are still a few things you should not do when implementing AI and ML.
Most people reading this are likely familiar with machine learning and the relevant algorithms used to classify or predict outcomes based on data. However, it is important to understand that machine learning is not the answer to all problems. Given the usefulness of machine learning, it can be hard to accept that sometimes it is not the best solution to a problem.
Chatbots suck. Watch Dr. Pieter Buteneers break down how and why they suck, how they might be improved in the future, as well as some of the failures he and his team experienced and how they learned from them. This is the talk he gave at Machine Learning Conference 2019.
Is machine learning the right choice for your business? In this article by Sagar Trivedi, find out what the possibilities are, and how using an ML model can save your organization time, help invest in the future, predict trends, and refine business solutions.
Machine learning can be implemented in different ways, one of which is reinforcement learning. What exactly is reinforcement learning and how can we put it to use? Before the upcoming ML Conference, we spoke to Dr. Christian Hidber about the underlying ideas and challenges of reinforcement learning, and why it can be suited for application in an industrial setting.
Can UX demystify AI? Ward Van Laer answers this question in his session at the ML Conference 2019. We invited him for an interview and asked him how to solve the black box problem in machine learning by merely improving the user experience.
In his column “Stropek as a Service”, SaaS expert Rainer Stropek talks about exciting aspects of the implementation, monetization and use of software as a service offerings. Today’s focus is on the connection between Software as a Service and Artificial Intelligence. How do SaaS projects benefit from Machine Learning?