deeplizard examines the difference between concatenating and stacking tensors together. We’ll look at three examples, one with PyTorch, one with TensorFlow, and one with NumPy.
In this video, deeplizard explores how to use TensorBoard to visualize metrics of our PyTorch CNN during training process.
In this video from deeplizard, learn how to build, plot, and interpret a confusion matrix using PyTorch. They also cover about locally disabling PyTorch gradient tracking or computational graph generation.
In this episode from deeplizard, learn how to build the training loop for a convolutional neural network using Python and PyTorch.
Watch this video on Deep Q-learning to implement your own deep Q-network in code.
PyTorch keeps growing and growing in acceptance. Here’s an interesting development from Facebook.
Reproducibility puts the science in the computer science of AI. It’s how researchers can prove their AI systems are robust and reliable. To support reproducibility for AI models, Facebook today released PyTorch Hub in beta, an API and workflow for research reproducibility and support. PyTorch Hub can quickly publish […]
Adam Paszke speaks at PyData Warsaw 2018 about PyTorch, one of the main tools used for machine learning research.
It’s been developed in beta mode for over 2 years, but this October, a release candidate for 1.0 version has been finally released. In this talk, Adam briefly introduces the library, and then move on to showcase the cutting edge features we introduced recently.
Amanda Silver shares how you can pair program with Live Share, how Python, Go, and Rust are used in VS Code, information about one of Code’s creators Erich Gamma, and how you can list and share your extensions.
In this video, deeplizard debugs the forward method and review the tensor shape transformations as well as the formula to calculate convolution output size.
Here’s an article about the latest PyTorch release.
Facebook AI Research announced the release of PyTorch 1.1. The latest version of the open-source deep learning framework includes improved performance via distributed training, new APIs, and new visualization tools including native support for TensorBoard. In a recent blog post, the Facebook team highlighted several improvements to the framework […]