Hardware Requirements for Machine Learning

Machine learning is a subset of artificial intelligence function that provides the system with the ability to learn from data without being programmed explicitly.

Machine learning is basically a mathematical and probabilistic model which requires tons of computations. It is very trivial for humans to do those tasks, but computational machines can perform similar tasks very easily.

Consumer hardware may not be able to do extensive computations very quickly as a model may require to calculate and update millions of parameters in run-time for a single iterative model like deep neural networks.

Thus, there is a scope for the hardware which works well with extensive calculation. But before we dive deep into hardware for ML, let’s understand machine learning flow.

There are four steps for preparing a machine learning model:

  1. Preprocessing input data
  2. Training the deep learning model
  3. Storing the trained deep learning model
  4. Deployment of the model

Among all these, training the machine learning model is the most computationally intensive task.

Now if we talk about training the model, which generally requires a lot of computational power, the process could be frustrating if done without the right hardware. This intensive part of the neural network is made up of various matrix multiplications.

Know More@ https://www.einfochips.com/blog/everything-you-need-to-know-about-hardware-requirements-for-machine-learning/

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