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?
When we are talking about the progress of machine learning, researchers have come a long way. But what does the future hold? James Wilson goes over some things you should keep an eye on.
What are developers and data scientists using Python for? The language continues to grow in the field of machine learning, web development, and neural networks. ActiveState recently examined the data from their customers and listed the top use cases by industry and what ML tools professionals are using.
Just how tiny can machine learning get? The latest hardware from Google brings machine learning to mobile and IoT devices. Coral is a platform for development with local AI and includes a powerful complete system dev board and a USB accelerator accessory device.
Worried your ML models might blab about proprietary data? Now, developers can keep their training data isolated from their machine learning models with TensorFlow Privacy. This Python library optimizes ML models without running into any data security or privacy concerns with differential privacy.
Automating routine, boring tasks sounds great. Automation promises to rid developers of scutwork and let them focus on the meaty details. However, as Oren Eini explains, this is a snare that causes more problems than it solves.
Are you interested in the future of AI, humanity, and technology? A new digital magazine ConditioHumana.io explores the topics of humanity’s future in the increasingly digital world. We invite our readers to explore articles and interviews with voices from computer scientists, machine learning experts, and leaders in the humanities.
It’s here! Take a sneak peek at the upcoming sessions; there’s a lot for ML developers of all experience levels. Get ready to learn all about the latest innovations in machine learning at ML Conference 2019! Buy your tickets now and save big.
2018 was a banner year for machine learning on GitHub. Projects like TensorFlow and PyTorch ranked among some of the most popular on the site, while Python carried on its dominance as a top programming language. It looks like the Octoverse is all about ML and we are 100% here for it.
Given the acceleration of change and increasing complexity of machine learning today, we can see many cases of high-profile samples of ML models not working as intended. In this article, SpringPeople Software shares some suggestions on how to make ML a better place.
Gradient-free optimization used to rely on custom implementation. But now, Facebook has just open sourced its Python3 toolkit for derivative-free optimization for improving machine learning parameters and hyperparameters. Optimize your models faster than ever with these tested algorithms!