Don’t give up on machine learning because of your low-end hardware

Don’t give up on machine learning because of your low-end hardware

Don’t give up on machine learning because of your low-end hardware. We collect more data and we use more complex models. Although it’s a good trend, things get more difficult for people starting with ML and using their low-end desktops. But it doesn’t mean that they cannot do cool things.

Year by year machine learning models are getting more advanced, more accurate and break performance records. We collect more data and we use more complex models. Although it’s a good trend, things get more difficult for people starting with ML and using their low-end desktops. But it doesn’t mean that they cannot do cool things.

Does it make sense to use your low-end PC?

You can find numerous articles about recommended workstations for machine learning or advices for building the perfect computer to train your models. Authors and people in comments claim that runnning ML training on your notebook makes no sense and it’s just waste of time.

It comes as no surprise that you can’t do deep learning research or run state-of-the-art models on a low-end computer. But what if you just want to get your hands dirty with machine learning, you’re not really sure if it’s for you and you don’t want to buy a new PC for that? Or if you simply can’t afford it?

You can play with toy examples

Unless your PC is literally 30 years old, you should be still able to process small datasets and train simple models on your hardware. Although this makes your machine learning experience limited, this may be enough to test popular frameworks or check your implementation of simple classifiers.

Datasets such as CIFAR-10 or MNIST can be used for classification tasks with low resources and if you prefer regression problems, there are also a lot of small datasets publicly available. If you’re limited by weak GPU rather than data storage or I/O limits, use simpler models instead of recent state-of-the-art architectures.

If that’s not the option or you want to build and run something bigger, there is another way.

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