Engineering expertise in IoT development, with deep-dives into firmware, cloud integration, and the technical challenges of building Internet connected devices.
In the previous article we learned the theory and basic implementation of one of the most popular reinforcement learning algorithms – Q-learning. Despite the obvious advantages of the classic approach with
Implementing Q-learning algorithm In the previous article in this series, we learned the basics and theoretical introduction of the Q-learning algorithm. We have theoretical knowledge, now it’s time to put it
Reinforcement learning (RL) is one of the most dynamic and fastest growing fields within artificial intelligence and machine learning. Unlike traditional supervised learning, where models are trained on a fixed dataset,
In the previous article we learned the theory and basic implementation of one of the most popular reinforcement learning algorithms – Q-learning. Despite the obvious advantages of the classic approach with
Implementing Q-learning algorithm In the previous article in this series, we learned the basics and theoretical introduction of the Q-learning algorithm. We have theoretical knowledge, now it’s time to put it
Reinforcement learning (RL) is one of the most dynamic and fastest growing fields within artificial intelligence and machine learning. Unlike traditional supervised learning, where models are trained on a fixed dataset,
IoT devices often serve quite specific purposes. They may operate in remote locations far from civilization, or they may be found right in our homes. The form an IoT device takes
IoT software development shifts the paradigm of traditional estimation. In traditional software development, estimation often revolves around logic, UI and database performance. However, in the IoT, the code is just the
Over the years IoT market has been flooded with various solutions, devices, and inventions. Many companies have been trying their hand at developing their own piece of hardware. Nowadays it’s fairly
