Effective hardware that doesn’t struggle with computations simplifies the work of a data scientist a lot, but it’s not always possible to upgrade your hardware.First of all, you can run Kaggle Kernels with GPU to speed up the training of deep learning models. Learn how to set it up. A great advantage of this option is that it is free. However, judging by some comments, this option is not always working. So, here are some other options.If you need more computer power, you can upgrade to Google Cloud AI Notebook directly in the Notebook. You can explore some options for free and also get a $300 credit. But when working with large amounts of data (for example, training a deep learning model), you will need to upgrade soon, and it’s quite costly. That is why some prefer to set up servers elsewhere. In that case, you will have to set up the environment on your own computer, and one of the most popular choices is Anaconda. If you decide to go for this option, use the tutorial by Faizan Ahemad on how to install it.Another popular option is to rent computer power from AWS, Microsoft Azure, Digital Ocean, or something similar. Platforms that are recommended by many specifically for deep learning are FloydHub and Crestle.ai.