Why is GPU Useful for Machine Learning and Deep Learning?

In the 21st century, machine learning and deep learning have become popular concepts. GPU (Graphics Processing Unit), with its improved computational abilities, plays a crucial role in providing this capability to meet businesses’ machine and deep learning needs.  

GPU helps process large amounts of data at high speeds, which makes it the perfect choice for dealing with complex data tasks like the analysis of images or speech recognition tasks. This blog post will delve into why GPU servers are so influential in machine learning and deep learning tasks, emphasizing their advantages over traditional CPU-based systems.  

What is a GPU?  

A specialized electronic circuit known as a Graphics Processing Unit (GPU) is made to rapidly alter and manipulate memory to speed up the creation of images in a frame buffer that is intended for output to a display device. GPUs are utilized in mobile phones, embedded systems, personal computers, workstations, game consoles, and workstations. 

Role of a GPU Server in ML & DL 

GPU servers were originally designed for video gaming but have found favor among AI researchers because of their ability to execute large numbers of mathematical operations in parallel. It makes them well-suited to the task of training neural networks, which is what underlies both machine learning and deep learning.  

Machine learning (ML) and deep learning (DL) are two branches of artificial intelligence (AI). In both, computer algorithms are used to learn from data, but in ML, the algorithms are shallow, while in DL, they are deep. A GPU is a type of processor that is well-suited to the execution of machine learning and deep learning algorithms.