Deep Learning - Computer Vision for Beginners Using PyTorch - What Is CUDA

The video tutorial introduces CUDA, a parallel computing architecture by NVIDIA, highlighting its ability to enhance computing performance by utilizing GPUs for mathematical calculations. It guides viewers on enabling GPU in Google Colab, using the Torch CUDA package for GPU computation, and checking GPU availability and details. The tutorial also covers identifying the assigned GPU's name, specifically the NVIDIA Tesla series, provided by Google Colab for learning purposes.

What is CUDA primarily used for?

• To enhance CPU performance

• To increase storage capacity

• To perform general-purpose calculations on a GPU

• To improve RAM speed

What is the first step to enable GPU in a Google Colab notebook?

• Select GPU in notebook settings

• Restart the browser

• Change the notebook theme

• Install a new package

Which package is used to work with CUDA tensor types in PyTorch?

• Torch CUDA

• Torch GPU

• Torch ML

• Torch CPU

How can you verify if a CUDA-enabled GPU is available?

• By checking the CPU usage

• By restarting the kernel

• Using the is_available method in Torch CUDA

• Using the device_name method

What is the significance of the NVIDIA Tesla series mentioned in the video?

• It is a type of CPU

• It is a universal deep learning accelerator

• It is a storage device

• It is a type of RAM