PyTorch provides GPU-accelerated tensor computation and neural network building. Library integrates with NumPy, SciPy, and Cython for easy extension. Uses tape-based autograd system for dynamic neural network behavior
Course divided into two volumes: Supervised and Unsupervised Deep Learning. Each volume focuses on three distinct algorithms. Tutorials emphasize intuitive understanding of Deep Learning concepts
20-hour course completed in 3 weeks at 6 hours weekly. 2,209 students have enrolled. IBM instructor provides shareable certificate. Available as part of multiple programs
PyTorch provides GPU-accelerated tensor computation and deep neural networks. Library integrates with NumPy, SciPy, and Cython for easy extension. Uses tape-based autograd system for dynamic neural network behavior
PyTorch requires macOS 10.15 or higher. Python 3.9-3.12 is recommended. Installation via Anaconda or pip package managers. Verification possible through sample code
PyTorch is a popular deep learning library with Tensor and autograd features. Tensor enables GPU migration and autograd calculates automatic gradients. Latest version is PyTorch 2.1, available since 2016