Traditional deep learning frameworks such as TensorFlow and PyTorch support training on a single deep neural network (DNN) model, which involves computing the weights iteratively for the DNN model.

Designing a DNN model for a task remains an experimental science and is typically a practice of deep learning model exploration. Retrofitting such exploratory-training into the training process of a single DNN model, as supported by current deep learning frameworks, is unintuitive, cumbersome, and inefficient.

In this webinar, Microsoft Research Asia Senior Researcher Quanlu Zhang and Principal Program Manager Scarlett Li will analyze these challenges within the context of Neural Architecture Search (NAS).

Whether it’s for reporting and offloading queries from production, there are things you need to keep in mind when using a Geo Replicated Azure SQL Database Readable Secondary.

Anna Hoffman speasked with MVP Monica Rathbun the challenges when it comes to performance tuning, what to keep in mind, and what to expect.