The goal of this course is to familiarize researchers in the life sciences with state-of-the-art deep learning techniques for microscopy image analysis and to introduce them to tools and frameworks that facilitate independent application of the learned material after the course. The following topics will be covered extensively during lectures, exercises, and project work: (2) A project-based phase, where students will work together with numerous TAs to apply the newly acquired skills to their own datasets. Faculty and TAs will assist the students in data preparation, problem formalization, network architecture design, tool selection, model training, prediction, reconstruction, and evaluation. Students will leave the course with an appreciation for the power and limitations of deep learning as well as broad knowledge of key tools that are needed in order to apply deep-learning methods to microscopy data. The goal of this course is to familiarize researchers in the life sciences with state-of-the-art deep learning techniques for microscopy image analysis and to introduce them to tools and frameworks that facilitate independent application of the learned material after the course.

Today we released .NET 7 Preview 5. This preview of .NET 7 includes improvements to Generic Math which make the lives of API authors easier, a new Text Classification API for ML.NET that adds state-of-the-art deep learning techniques for natural language processing, various improvements to source code generators and a new Roslyn analyzer and fixer for RegexGenerator and multiple performance improvements in the areas of CodeGen, Observability, JSON serialization / deserialization and working with streams. If you're on macOS, we recommend using the latest Visual Studio 2022 for Mac preview. Now, let's get into some of the latest updates in this release. The goal of observability is to help you better understand the state of your application as scale and technical complexity increases. The exposed methods can be used in performance critical scenarios to enumerate the Tag objects without any extra allocations and with fast items access.