Simply put, Deep Learning is a machine learning technique that teach computers to learn by example, just like humans do. This technology enables the achievement of results that were not possible before, such as voice-controlled equipment and driverless vehicles, for example. But how does it work, what the differences between Machine Learning and Deep Learning, and where it can be used?

How does Deep Learning achieve its results?

To achieve its results, deep learning computer models learn to perform classification tasks from data sets ranging from images, sound, and text. With proper training, deep learning models can even surpass human performance.

Deep learning methods use Neural network architectures (a neural network is an adaptive system that learns from data in a layered structure that resembles a human brain).

Is Deep Learning the same as Machine Learning?

The biggest difference is that in machine learning the model needs to be taught manually, while in deep learning the model can learn on its own, doing it automatically. Another key difference is that deep learning algorithms keep learning on their own, improving as the size of data increases.

Use Cases for Deep Learning

A few of the use cases for deep learning are:

• Automated Driving – With the use of deep learning, automated vehicles can detect objects, such as traffic signs, traffic lights and pedestrians, helping avoid accidents.
• Industrial automation – There can be significant improvements to worker safety around heavy and dangerous machinery by having automatic detection when people or objects are for example within unsafe distances of machines.
• Electronics (IOT) – Home assistant devices that can respond to your voice and know your preferences can be powered by deep learning.

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