Deep learning : how to implement this Artificial Intelligence technology

Deep learning, is an AI technology inspired of machine learning. That approach, based on statistics allows machines to learn thanks to data. These machines are then able to resolve tasks without a previous programming. Algorithms learn by themselves. They are autonomous and are improving without any human intervention. But, what is really deeplearning ? What are the right infrastructures to implement that type of technology ?

Deep Learning : What is it ?

The implementation of deep learning (just as images recognition for instance) involves the creation of "artificial neurones" connected with each others. When we talk about deep learning, it refers to a large amount of "connected neurones".

Just as human neurones : they share a huge volume of data et come with the right solution to solve a problemDeep learning is not a very recent technology. Indeed, it began in the 50's with the modelization of a neurones' network to better understand human brain. 20 years later, a learning algorithm is created. Nevertheless, we had to wait until 2012 to use that artificial network to resolve problems. The development of that network is partially possible thanks to Big Data. The more data you have, faster the machine will learn. On the other hand, the improvment of AI tech. has also boosted the development of deep learning thanks to faster GPU. Moreover, the accumulation of knowledge within human brain deeply helped the construction of the "artificial neurones network" and so deep learning.

What is the concret purpose of Deep learning ?

Deep learning is mostly used in image and sound processing.. Moreover, it is deep learning that is used for facial recognition on Facebook, Face ID or Skype. In that case, it allows to translate a spoken conversation. However, other applications could be considerd, as text recognition for translate or write content, médical diagnosticor robotic. The usages of deep learning are numerous? It is no longer useful to indicate to the machine the features to identify considering that it is already able to learn thanks to basic ressources.

In any case, to obtain the right answers from a neuronal network, it must be trained.. Let's guess that we want to identify pictures which shows a cube thanks to deep learning. Thousands of images are submitted to the computer where a cube is identified, in all the colors, under different angles... The pictures showing a cube are manually identified, then, the machine compares its results to the humans', to learn from mistakes or to acknowledge right answers.

Deep learning involves a lot of practice,to obtain a 100% matches result. That method of learning is called supervised learning. But it is also possible to implement non supervised learning. It consists in allowing the machine to learn on its own without any indication, After some practice, the machine will be able to identify specicif things on pictures.

If you have any projects in deep learning for your business, you must plan tailored infrastructures.Which specs. are precisely required ? Which web hoster should you prefer ?

The necessity to have a tailored infrastructure

As we have already said, the machine needs an enormous volume of data to be efficient. It also involves a large computing power. So, to be sure to be able to support any tasks, you must opt for an adapted server.

IKOULA provides two ranges of high performance dedicated servers adapted to deep learning and more generally machine learning.

  • Xtreme range : X-Silver Core and X-Gold Core servers are more powerful thanks to their bi-processors but also provides more storage and more RAM power..
  • GPU range : GPU Master, GPU Master XL and RAID MAster present high performance. They are equiped with a powerful Nvidia graphic card and a Fusion IO for Raid Master allowing flash storage.
  • Cloud IKOULA One, l’offre cloud que propose IKOULA, permet de déployer des instances spécialisées CPU, basées sur des machines cadencées à 3Ghz, particulièrement adaptées pour des utilisations intensives de processeurs.

Thanks to these dedicated infrastructures, it possible to create an autonomous network to push further your large scope projects.

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