deep reinforcement learningmachine learningpower systemsmart gridsDue to increasing complexity, uncertainty and data dimensions in power systems, conventional methods often meet bottlenecks when attempting to solve decision and control prob- lems. Therefore, data-driven methods toward solving such prob- ...
Deep Reinforcement Learning-Based Battery Management Algorithm for Retired Electric Vehicle Batteries with a Heterogeneous State of Health in BESSs A battery energy storage system allows for the use of retired lithium batteries for applications such as backup power in homes, data centers, etc. In a....
With the advancement of research, the increasing computing power of ML-oriented hardware such as GPUs and TPUs, and the widespread adoption of open source machine learning tools such as Tensor-Flow and PyTorch, there has been a huge increase in research output in machine learning and its appli...
Deep learning requires both a large amount of labeled data and computing power. If an organization can accommodate for both needs, deep learning can be used in areas such as digital assistants, fraud detection and facial recognition. Deep learning also has a high recognition accuracy, which is c...
Machine learning models need inference engines and good datasets. OpenVINO and Anomalib are open toolkits from Intel that help enterprises set up both.
Advanced applications of computer vision Thanks to advances in deep learning, computer vision is now solving problems that were previously very hard or even impossible for computers to tackle. In some cases, well-trained computer vision algorithms can perform on par with humans that have years of ...
Deep learning is a subset of machine learning that uses multilayered neural networks, to simulate the complex decision-making power of the human brain.
Deep learning, a subset of machine learning, is being deployed in new and innovative ways all the time. Check out 20 different applications of deep learning.
As a secondary contribution, we adapt the FCRBM architecture for energy prediction problems by merging the style and feature labels into one, and by rewriting the equations and the derivatives of the learning rules according to the new configuration of the model. Both, CRBM and FCRBM, are ...
Learn what deep learning is, what deep learning is used for, and how it works. Get information on how neural networks and BERT NLP works, and their benefits.