What is deep learning?

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Deep learning is a technological model of this new algorithm that we talked about in the introduction, and which is based on the connections made by neurons as they do in the human brain.

All this is based on the fact that machines have tried to imitate the best machine in the world: the brain; they have followed a linear learning algorithm, while deep learning is marketing list of rv owners becoming increasingly complex.

This means a revolution in the world of machines, as the hierarchies of neurons are becoming increasingly complex with deep learning.

In short, deep learning is about learning from examples, which is natural for the human brain.

However, with deep learning, a computer model can learn to perform direct classification activities after seeing images, text or sound.

These models can achieve precisions in results that sometimes even exceed the performance of the human brain.

So, in summary, these models are trained using a large set of labeled data and neural network dynamics that contain various layers, as we explained previously.

But what is the benefit of all this?



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How does deep learning benefit?
The main benefit of deep learning is the precision with which it operates, which we mentioned before, it is incredible that it even surpasses humans.

With such precision, the results obtained are impressive, so user satisfaction and expectations are fully met.

This is beneficial in minimizing some tasks and helping in applications critical to user safety.

On the other hand, we leave you with two great reasons to think that deep learning is reliable and important for this new technological era:

Deep learning requires large amounts of labeled data, so, for example, developing a self-driving car requires millions of images and thousands of hours of video footage.
Significant computing power is required, and high-performance GPUs thus have a parallel dynamic that is beneficial for deep learning. By combining this with clusters or cloud computing, the amount of time to train a deep learning network can be reduced.
Differences between deep learning and machine learning
Many people may confuse deep learning with machine learning, but the truth is that they are not the same and respond to different characteristics and functions that we will see below.

While in machine learning aspects are manually selected with a classifier to classify them, in deep learning these processes are automatic.
Within a machine learning workflow, it begins with the manual extraction of the most relevant aspects of the images, but with a deep learning workflow , these relevant aspects are extracted directly from these images.
Deep learning is about complete learning, that is, the data is unprocessed and the machine must learn on its own or automatically.
Deep learning algorithms scale with data, but shallow learning requires convergence. Convergence indicates that machine learning methods can reach a plateau at a certain performance level , such as when adding more training data to the neural network.