CPU vs GPU and its use in Machine Learning

Speed: GPUs have a high number of cores, which makes them well-suited for parallel processing tasks such as matrix operations. This makes them faster for certain types of machine learning tasks, such as training deep neural networks. Cost-effectiveness: Training large machine learning models can require a lot of computational resources, and using GPUs can be more cost-effective than using CPUs for these tasks, as they can process large amounts of data much faster. Large-scale training: Training deep neural networks requires a lot of data and computational power, which makes GPUs ideal for this type of work. By using GPUs, researchers and practitioners can train much larger and more complex models than they would be able to with CPUs alone.