Artificial Intelligence is the outer shell that can encompass a variety of technologies where Machine Learning and Deep Learning operate underneath.
To clarify, consider this example where you have images with different animals (dog, hummingbird, monkey, elephant, etc.) that must be appropriately categorized.
How can this be done? In machine learning, an expert must establish a hierarchy of essential characteristics that are indispensable to distinguish one animal from another, such as the presence or absence of ears or wings.
In deep learning, however, the algorithm, not the hierarchies experts define, determines which characteristics are essential for animal images to be correctly categorized.
In this way, through gradient descent and retro programming, the deep learning algorithm adjusts and makes accurate predictions to categorize a new animal photo.
What Are The Advantages Of Machine Learning And Deep Learning For Companies?
While different in specific ways, machine learning and deep learning significantly contribute to companies. Check out some.
Several applications use deep learning. For example, we can cite applications in the medical field that provide greater productivity for teams during developing medicines and diagnosing diseases, such as cancer.
Cost Reduction Through Machine Learning
Certain companies, such as those that deal with a more significant number of customers, dedicate part of their budget to hiring a personalized support team; after all, they need to provide remote assistance to their customers.
This is where they benefit from deep learning. Personalized support provided by people is no longer necessary, as the company can adopt a virtual assistant to schedule meetings, for example, as well as answer customer questions.
Learn With Data
You can also understand customer behavior with deep learning. A simple way to know how this works is this: from the moment a potential customer accesses e-commerce, for example, their data is collected.
Using this data, the company can create a better user experience within the virtual store. It optimizes the site to create an immersive shopping experience that generates a single result: conversion into a sale.
Amazon and eBay are examples of e-commerce sites that learn from data and thus become more efficient and promising.
Where Is Deep Learning Applied?
Although it is unknown to some people, deep learning is present in many aspects of human life. It, for example, is used to improve user experiences in online search results, predict equipment failures, filter spam in emails and even analyze feelings through texts published on social networks.
Its application can also be seen in personalized recommendations, such as those content recommendations on streaming platforms like Netflix. In it, the algorithms show the user content that will interest him, and they do this using two types of filtering.
The first is collaborative filtering. Netflix has a multitude of users, and many of them have similar profiles. Therefore, based on these similar profiles, the algorithm recommends certain types of series and movies to all users within the same profile type.
The other type is content filtering. In this case, the algorithm already knows what the user likes to watch on the platform and recommends other content similar to what he already consumes. An example of this happens when users finish watching a series. Still, during the final credits, the algorithm shows on the screen the recommendation of another series that the user will probably like to watch.
Throughout this content, we discussed with details and examples what deep learning is, how this technology works and its importance.
Also, since deep learning and machine learning are branches of artificial intelligence, there can be some confusion between the two. This, however, was clarified by citing the main distinction between them: the way they learn.
Finally, it dealt with how deep learning and machine learning are used by companies, highlighting the advantages they provide, such as increased productivity and cost reduction.