Prime things to look for a machine learning device for Starters.

Artificial Intelligence technologies such as machine learning and deep learning demand use of large amounts of data and complex algorithms that require powerful computation hardware. This article will help you finalize some key points for these tasks.

GPU.

GPUs are micro processing chips primarily designed for handling graphics. GPUs have become popular in Machine learning and Deep Learning field mainly for their  ability to handle simultaneous computations faster than CPUs. Necessarily, GPUs have a large number of cores and high memory bandwidth and are thus suites for multiple parallel processing of large amounts of data. This has been boosted by efforts to develop AI-based GPU framework such as CuDNN  and parallel computation APIs like CUDA by NVIDIA. Such frameworks and APIs allow scientists to leverage GPU parallelism for deep learning tasks.

What is CuDNN and  CUDA?

NVIDIA CUDA Deep Neural Network (CuDNN) is a GPU-accelerated library of primitives for deep neural networks. It provides highly tuned implementations arising frequently in DNN application. In simple words CUDA is NVIDIA's language/API for programming on the graphics card.

What to look for in a GPU? Here are the points 👍-

1- Choose a higher memory bandwidth (speed of video RAM) within your budget.

2- You might be dealing with large amounts of data, go for a higher number of cored as it dictates the speed of processing data.

3- Consider the processing power of the GPU if the Computation time matters for you.

Which GPU should I then buy?

NVIDIA GPU is preferable because of the available frameworks and APIs (CUDA and CuDNN) compatible with major deep learning framworks such as Tensor-Flow and PyTorch. The latest generations of NVIDIA GPUs such as the GeForce RTX based on Turning architecture are AI-enabled with Tensor cores which makes them suitable for deep learning.

RAM.

RAM is another important factor. The larger the RAM the higher the amount of data it can handle, leading to faster processing. With more RAM you can use your machine to perform other tasks as the model trains. Although a minimum of 8gb RAM can do the job, 16GB RAM and above is recommended for most deep learning tasks.

Actually, selecting RAM for depends on the problems you are solving or your needs. Professionals do use machines with RAM ranging from 64GB to 256GB. AI tasks like machines learning and deep learning and deep learning are better handles by cloud service such as Google Cloud, Azure and AWS. Patriot Viper Steel is currently the best brand for RAM.

CPU.

For CPU, a minimum of 7th generation (Intel Core i7 processor) is recommended. However, getting Intel Core i5 with Turbo boosts will be sufficient. Select the right combination of CPU and motherboard that match your GPU specifications. The choice of the number of PCle lanes (Pcle lanes determine the speed of transferring data from CPU RAM to GPU RAM) should also be taken into consideration (4-16 PCle lanes is best for most deep learning tasks). A good CPU with many cores can boost performance significantly. Intel Core i7 is actually a great processor, it offers you a processing speed of up to 4.5 GHz which if you are newcomer is a best choice because Intel Core i9 is a little expensive especially when you are a beginner and Core i5 may feel slow as you improve your skills.

Storage.

Due to the increasing size of deep learning and machine learning datasets, it requires higher storage capacity. For example, Imagenet, one of the most popular datasets for deep learning, is 150GB in size and consists of more than 14 million images across 20,000 categories. Although SSD is recommended for its speed and efficiency, you can get an HDD at a relatively cheaper price to do the job. However, if you value speed, price and efficiency then a hybrid of both is the best option.

You should go with at least 512 of  storage. If you have a system with SSD a minimum of 256GB is advised. Again, if you have less storage you can always go for Cloud Storage Options. There you can get machines with high GPUs even.