Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming ...
Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years ...
PyTorch 1.10 is production ready, with a rich ecosystem of tools and libraries for deep learning, computer vision, natural language processing, and more. Here's how to get started with PyTorch.
本书分为三部分，共19章，第一部分为PyTorch基础，第二部分为深度学习基本原理，第三部分是实战内容。 第一部分（第1-4章 ...
Religious wars have been a cornerstone in tech. Whether it’s debating about the pros and cons of different operating systems, cloud providers, or deep learning frameworks — a few beers in, the facts ...
TAO Toolkit is a Python package hosted on the NVIDIA Python Package Index. It interacts with lower-level TAO dockers available from the NVIDIA GPU Accelerated Container Registry (NGC). The TAO ...
If you have trouble following the instruction below, feel free to join OSCER weekly zoom help sessions. If you're doing deep learning neural network research, pytorch is now a highly recommended, ...
Dr. James McCaffrey of Microsoft Research details the "Hello World" of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset. The "Hello World" of image ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results