Training neural networks requires performing a huge number of mathematical calculations. Each layer of the network processes many inputs, multiplies them by weights, sums them up, and applies activation functions. When you have millions of parameters and a large dataset, this process must be repeated again and again during training. On a standard computer processor (CPU), this can be very slow and may even take weeks for complex models. This is why specialized hardware is so important.

Graphics Processing Units (GPUs) are designed to handle many operations in parallel. Unlike CPUs, which are optimized for doing a few tasks very quickly, GPUs can do thousands of small calculations at the same time. This parallelism is a perfect match for training neural networks, since most of the required operations (like matrix multiplications) can be done simultaneously. Using GPUs makes training much faster and allows us to work with larger datasets and deeper networks than would be possible with CPUs alone.

Tensor Processing Units (TPUs) are another kind of specialized hardware developed specifically for machine learning tasks. TPUs are even more specialized than GPUs, they are built to handle the types of mathematical operations that neural networks use most often, such as tensor (multi-dimensional array) computations. Because of this specialization, TPUs can sometimes train models even faster and more efficiently than GPUs, especially in large-scale training.

The importance of GPUs and TPUs in training is that they make deep learning practical. Without them, training large neural networks would take an impractical amount of time and computing power. With them, researchers and engineers can experiment more quickly, try different models, and train on massive datasets. In short, GPUs and TPUs are the key tools that make modern connectionist AI possible, turning training from something that might take months on a CPU into something that can be done in hours or days.

https://www.geeksforgeeks.org/machine-learning/comparing-cpus-gpus-and-tpus-for-machine-learning-tasks/?utm_source=chatgpt.com

https://mljourney.com/gpu-vs-tpu-for-training-machine-learning-models/?utm_source=chatgpt.com