Machine Learning VS Deep Learning: What Are Their Differences?

Artificial Intelligence is a particularly broad area of research. Used in the medical, digital and industrial fields, this technology offers numerous development opportunities. Based on an artificial system, this technology aims to recreate an intelligence system similar to that of the human brain; so as to carry out certain complex tasks even inaccessible for Men. This area of research is divided into different disciplines, among which are Machine Learning as well as Deep Learning. These two disciplines are specialties of AI and focus on very specific subjects of study. Back to these areas of Artificial Intelligence.

 

Machine Learning and Deep Learning: Definitions

 

Machine learning

 

Among the fields that make up Artificial Intelligence, machine learning is concerned with the learning capabilities of a machine. The objective of machine learning is to set up a reasoning system that is formed independently. The goal is therefore not to program the machine but to give it several examples and data so that it can form itself. Ultimately, machine learning aims to recreate behavior and reasoning to solve a given problem.

 

Deep learning

 

Deep learning corresponds to “learning in deep neural networks”. This discipline corresponds to machine learning methods which are therefore based on deep neural networks. Deep learning is classified as a sub-domain of machine learning.

 

What are the differences between the two?

 

On a daily basis, artificial intelligence is already present in many areas. If we can already see significant progress, it is important to know that the possibilities for development are numerous. To make the most of Artificial Intelligence, it is important to take an interest in the potential of machine learning and deep learning. Aiming to recreate the capacities of a human brain, these two techniques nevertheless differ in certain points.

 

Machine Learning: ranking and prediction

 

 

Sub-domain of Artificial Intelligence, machine learning aims to classify the different data analyzed and make certain predictions based on this data processing. The machine learning algorithm is based on a search for recurrence in the analyzed data in order to be able to identify a certain model. From this classification and the observations made, machine learning will be able to establish certain predictions which will be more or less correct depending on the number of data analyzed and the level of learning of the machine. For companies for example, the use of machine learning can allow segmenting the different types of customers. This clustering technique is a real asset to adapt its production and respond as precisely and as quickly as possible to customer needs.

 

Deep Learning: precise data analysis

 

 

As a sub-domain of machine learning, deep learning enables deeper data analytics. Operating like a neural network, the information processed by this system becomes more and more precise as the layers of neurons are numerous. This type of technique, more successful than machine learning, is particularly suitable when the masses of data to be processed are immense. In this sense, deep learning differs from machine learning by the fact that this technique accentuates the precision of the analysis and has the capacity to process a larger number of data.

 

Complementary techniques

 

Today, the use of machine learning is an increasingly widespread technique, and this, in many sectors of activity. Older than deep learning, machine learning is a technique that is better mastered and whose results are easy to read. The use of Artificial Intelligence and the different techniques that compose it therefore depends on different factors. On the scale of companies, the choice of the use of deep learning concerns in particular the largest organizations which have huge databases and require big data analytics. Requiring a much larger amount of information than for machine learning, deep learning is therefore more suitable for large structures which also have the means to invest in this type of technology. Indeed, being still in development, deep learning requires significant investments to develop and offer the best possible results.

 

In view of the objectives of each of these techniques, we can consider that they are partly complementary since deep learning appears to be an improvement of machine learning. Today, machine learning is increasingly used by companies with limited amounts of data to be processed. In the future, experts consider that machine learning could become a simple tool for carrying out tasks to optimize already existing operations.

 

Regarding Artificial Intelligence, we note that the prospects for evolution and improvement are numerous. Today, deep learning appears to be the future and will make it possible to refine the information gathered thanks to an even more precise analysis of the masses of information which are also tending to increase. To continue progress in the field, it seems necessary to pool knowledge and all the investments that go in this direction. The challenges of perfecting these techniques are therefore primordial and involve rethinking the place of humans alongside artificial intelligence.