Teacherhighlight course mooc presentation weather ecmwf deep learning machine learning
The modern-day machine learning boom was caused by deep learning. Advances in many fields especially those covered in the media, use deep neural networks. The main advantage of deep learning is its versatility and composability, where we develop purpose-built architectures from nowcasting to long-term climate prediction.
These neural architectures are composed of building blocks in machine learning. We will cover architectures such as convolutional and recurrent neural networks, from long short-term memory networks to modern state-of-the-art transformers and discuss their strengths and limitations in analysing complex weather and climate data. We will also delve into the small details that make neural networks work in the real world of weather and climate prediction. By the end of this lesson, learners will have a good overview of the different deep learning architectures and their potential for improving weather and climate prediction.Visit Site Fork on Github See also: Teaching Mooc