🧠 What is Deep Learning?
To understand how Deep Learning works, we must first define it. Deep Learning is a technique based on neural networks that try to recognise and relate complex patterns that behave in the same way. It involves generating a computational network from texts or images that do not have attribute processing.
The name neural networks come from the resemblance to the way a brain works. We create algorithms that learn in the same way that our brain learns. Deep Learning tries to create neurological networks in the same way that a biological neurological network does, it is created from inputs that we decide and from there, the computational networks that we are interested in knowing are created.
Deep Learning offers 3 major advantages:
- It provides a flexible and universal framework.
- It can be used for all types of tasks (supervised, unsupervised and reinforcement).
- It forms an end-to-end system, without the need for intervention.
Difference between Machine Learning and Deep Learning
You may ask yourself what the difference is and what we can say is that both belong to the Artificial Intelligence group, which would be the field of computer science focused on creating programs that can emulate intelligent mechanisms such as abstraction, learning, pattern recognition and planning. And Machine Learning is within Artificial Intelligence.
Machine Learning is the ability you give computers to learn by recognising patterns without necessarily programming them. Whereas Deep Learning is a subfield of Machine Learning in which you specialise in complex patterns in information, and use neural networks for learning.
The difference is that in Machine Learning you have to go through the steps and extract the attributes to keep the relevant ones. In Deep Learning you skip the step of extracting, you don’t look for what is relevant and what is not to perform the classification.
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