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).

On my livestream today, I briefly mentioned that I was excited about the potential of Blazor for web development.

Fortunately, The Blazor Day is the online event around Blazor technologies.

It’s organized by organized by 3 MVPs and it looks awesome.

Walkthrough of newly released PyTorch Learn the basics tutorial with PyTorch Developer Advocate Suraj Subramanian.

Learn more:

  1. Learn the basics – https://aka.ms/PyTorch/LearntheBasics
  2. PyTorch.org – https://pytorch.org/
  3. PyTorch on YouTube – https://www.youtube.com/pytorch

Related links:

PyTorch is one of the most popular open source machine learning framework that accelerates the path from research to production deployment.

In this tutorial, Dmytro Dzhulgakov, core contributor for PyTorch, will go through an introductory level hands-on tutorial for building fashion recognizer.

Related links:

Adding GPU compute support to Windows Subsystem for Linux (WSL) has been the #1 most requested feature since the first WSL release.

Learn how Windows and WSL 2 now support GPU Accelerated Machine Learning (GPU compute) using NVIDIA CUDA, including TensorFlow and PyTorch, as well as all the Docker and NVIDIA Container Toolkit support available in a native Linux environment.

Clark Rahig will explain a bit about what it means to accelerate your GPU to help with training Machine Learning (ML) models, introducing concepts like parallelism, and then showing how to set up and run your full ML workflow (including GPU acceleration) with NVIDIA CUDA and TensorFlow in WSL 2.

Additionally, Clarke will demonstrate how students and beginners can start building knowledge in the Machine Learning (ML) space on their existing hardware by using the TensorFlow with DirectML package.

Learn more: