Which Is The Best Laptop For Machine Learning and Artificial Intelligence?

With the rise in analysis platforms across every sector, leading big tech companies and startups are integrating Machine Learning and Deep Learning in their existing system. This advancement has led to a major revolution in the hardware required for these setups. But what about students and beginners who are trying to enter this domain, who are willing to take some time off and are working towards entering the Artificial Intelligence sector, what kind of machine should they invest on depending on the budget they have? We present a variety of options that are available in the market. To have a personal machine for the projects you have in mind, one needs to choose between mobility of their machine, that is, if they want to carry it around - a laptop. Or a machine for training the algorithms alone - a customized desktop at home or work.

What are the minimum requirements of the machine?

Note: Only Nvidia GPUs have the CUDA extension which allows GPU support for Tensorflow and PyTorch.

High End Laptops

Budget Laptops

You can also find budget laptops with CUDA enabled Nvidia GPUs for deep learning. The gaming laptops with these specs are essentially not for gaming but can also be used for deep learning. With a downgrade in RAM and CPU, one can opt this for training.

One can build their own PC for a lower price compared to these pricy laptops. This article will give you a brief information on how to customize a PC for deep learning. With the advancement in tech, the GPU prices are skyrocketing with addition of new Nvidia Tesla GPUs. It is better to invest on an high end GPU by compromising the CPU for deep learning purposes.

What are External GPU Docks?

An external GPU is an addition to the existing laptop where one can buy the external graphic dock extension like the Asus ROG XG Station Graphics Dock. This is can be customized based on your specifications.

Conclusion

By having one of these laptops, one can build, train and test their own deep learning models in a short span of time. When it comes to deep learning, tuning the hyper parameters being a tedious and time consuming task, it is advisable to have a power powerful machine to save time.