What Is Deep Learning and How It Benefits Us
Posted: Sat Dec 21, 2024 3:45 am
Deep learning is a technology that is based on a network of neural connections, but you may be wondering: How does this connection occur?
The brain is the vital organ of any human being and it is estimated that it contains between fifty and more than one hundred thousand neurons.
Of these neurons, about ten billion correspond to the so-called cortical gambling email list cells, which are responsible for sending signals to the rest of the body through synaptic connections.
After knowing all this information, more and more machines are trying to imitate the functioning of the human brain with the help of a network of artificial neurons, with which the dynamics would be more or less the following:
There are green neurons that are responsible for the inputs and therefore receive the information sent, while the blue ones are hidden and contain the intermediate calculations of the neural network. These neurons are found in layers.
Then, there are the yellow ones that are responsible for the outputs that carry the result of the processed information.
On the other hand, it is normal to have an input layer, an output layer, and other hidden layers.
This means that the more hidden layers there are, the more complex and intelligent the neural network is and the better the predictions will be, but it is also much more complicated to design such a model.
All these neurons are obviously connected to each other with a number called Bias, which indicates the importance of the network, but the weight of the neurons is what really indicates the relevance of their connection.
Activation is carried out by adding the numbers performed in previous operations, this is transformed into a formula and becomes a new number.
However, there are different types of neural network dynamics, among which we can highlight:
The restricted Boltzmann machine (RBM).
Recurrent neural networks (RNN).
The Deep Belief Network (DBN).
Convolutional neural networks (CNN).
In any case, all of this is about algorithms that can be divided into four main ones: object recognition, voice recognition, text processing and image recognition.
Does this sound familiar? Well, it probably does, because Facebook uses it to detect faces in photos and Google Translate uses it to process texts in another language that you send them.
This is where we get into the subject that captivates us today, which is deep learning , so if this topic is of interest to you, we ask you to continue with us.
The brain is the vital organ of any human being and it is estimated that it contains between fifty and more than one hundred thousand neurons.
Of these neurons, about ten billion correspond to the so-called cortical gambling email list cells, which are responsible for sending signals to the rest of the body through synaptic connections.
After knowing all this information, more and more machines are trying to imitate the functioning of the human brain with the help of a network of artificial neurons, with which the dynamics would be more or less the following:
There are green neurons that are responsible for the inputs and therefore receive the information sent, while the blue ones are hidden and contain the intermediate calculations of the neural network. These neurons are found in layers.
Then, there are the yellow ones that are responsible for the outputs that carry the result of the processed information.
On the other hand, it is normal to have an input layer, an output layer, and other hidden layers.
This means that the more hidden layers there are, the more complex and intelligent the neural network is and the better the predictions will be, but it is also much more complicated to design such a model.
All these neurons are obviously connected to each other with a number called Bias, which indicates the importance of the network, but the weight of the neurons is what really indicates the relevance of their connection.
Activation is carried out by adding the numbers performed in previous operations, this is transformed into a formula and becomes a new number.
However, there are different types of neural network dynamics, among which we can highlight:
The restricted Boltzmann machine (RBM).
Recurrent neural networks (RNN).
The Deep Belief Network (DBN).
Convolutional neural networks (CNN).
In any case, all of this is about algorithms that can be divided into four main ones: object recognition, voice recognition, text processing and image recognition.
Does this sound familiar? Well, it probably does, because Facebook uses it to detect faces in photos and Google Translate uses it to process texts in another language that you send them.
This is where we get into the subject that captivates us today, which is deep learning , so if this topic is of interest to you, we ask you to continue with us.