Deep learning is a subfield of
machine-learning algorithms inspired by brain structure and function called
artificial neural networks (ANN).

Deep learning (deep neural network)
uses multiple layers between input and output layers to progressively extract high-level
features.

A deep neural network (DNN) finds
the right mathematical processing to convert inputs into outputs, whether this
is a linear or non-linear relationship.

Deep learning (DL) is a branch of Machine Learning (ML) based on artificial neural networks (ANN) |

##
What is Deep Learning
(Deep Neural Network) and How Does Deep Learning Work?

### What is Deep Learning?

**Deep Learning (DL)**is a very important branch of Machine Learning (ML). In deep learning, a computer model tests logarithms and programs and learns to improve and develop them on its own.

Deep learning is primarily
concerned with developing algorithms that enable a computer to perform
classification tasks directly from images, texts, and audios or perform
difficult tasks that require a deep understanding of the data and the nature of
its work.

Deep Learning is sometimes referred
to as Deep Neural Networks, as it relies on Artificial Neural Networks.

Deep learning models can achieve
sophisticated accuracy that sometimes exceeds human performance, and models are
trained using a wide range of disaggregated data and neural network structures
that contain multiple layers.

The characteristics of deep learning
algorithms are their ability to learn and automate tasks without explicit
programming.

Explicit programming here means
writing certain commands and conditional tools to test data in order to arrive
at a particular result or extract data features by data scientists.

Deep learning algorithms can
automatically extract the most important features and patterns of repetitive
data by looking at a lot of input data and then analyzing it to find direct or
indirect links between the input data and the desired output.

Today, the uses of deep learning
technology include all kinds of big data analytics applications, especially those
focus on language translation, natural language processing
(NLP), stock market trading, and Forex trading signals, medical diagnostics,
image capture, and network security.

Deep learning algorithms are in
contrast to machine learning algorithms that require a deep
understanding of data and a great deal of effort to derive its features and
patterns manually by data scientists.

####
**Machine Learning (ML) and Deep Learning (DL)**

Machine Learning (ML) is a current
application of AI-based technology that gives systems the
ability to automatically learn from experience and improve the programs and
algorithms without being explicitly programmed.

Deep learning is a special type of
machine learning that relates to algorithms inspired by the human (animal)
brain called artificial neural networks (ANN).

Until now, most machine learning
algorithms (such as Support Vector Machine and Logistic Regression) were
dependent on data scientists' intervention to derive data features and patterns
before they were consumed by these algorithms.

These algorithms learn by monitoring
a large number of situations and focusing on pre-set patterns and feature
manually, in addition to the fact that these algorithms depend on the
intervention of data scientists.

The architecture of these algorithms
is superficial and contains at most two layers of nonlinear transformations.

Although these algorithms succeeded
in solving many problems with simple structures, they were not effective in
solving problems with complex structures such as natural languages, visual
scenes, and audio signals.

These problems require a deep
understanding of the data and its patterns and the making of numerous and
complex nonlinear transformations in order to convert the data in its natural
form, such as the image, to the desired output, such as the description of the
input image.

For this, deep learning technology
emerged in the world.

####
How Do Machine Learning Algorithms Work?

Most machine learning algorithms
rely on two basic steps: observation and simulation (prediction).

This is in a group of algorithms that
rely on supervised learning or learning by observing previously known outcome
events.

Machine learning algorithms first
monitor the input data and try to derive distinct patterns and characteristics
of the data and then simulate the behavior of functions based on the
connections and relationships formed by observing the process of converting
input data into specific outputs.

Here we distinguish between the
term

**machine learning algorithms**and**machine learning models**.
The algorithms are the necessary
steps and methods used in the learning process and a model is a code structure
that uses an algorithm to perform a particular task.

For example, deep learning
algorithms can accurately predict house prices after looking at a large number
of homes, their features, and prices, so the algorithm can automatically detect
the most important features that determine the price of a house.

These features and patterns are then
used to predict the prices of other houses based on the characteristics of each
house.

Therefore, the more cases monitored
during the model training process, the more likely and accurate the forecast
results will be improved.

#####
Deep Learning Architectures

The most popular

**deep learning architectures**include convolutional neural networks (CNNs), deep stacking networks (DSNs), recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent unit (GRU).####
How Does Deep Learning Work?

Deep learning algorithms consist of
a set of non-linear processes with deep structures (hence called deep
learning).

What distinguishes the architecture
of these algorithms is that they are inspired by the neural networks in the
human brain.

Therefore, Deep learning
architectures are also called artificial neural networks.

In deep learning algorithms, the
learning process takes place in two ways.

**Supervised Learning:**The machine learning process is marked based on a set of predefined data with the correct result, such as predicting house prices after reviewing a large number of nearby house prices.

**Unsupervised Learning:**The machine learning process is done using a set of data but without recognizing the correct result in advance, such as grouping data points based on a pattern automatically detected by an algorithm.

In deep learning, both supervised
learning and unsupervised learning may be used to train the

**Artificial Intelligence (AI)**.**Neural network architecture**(we mean artificial neural networks) is generally composed of three types of layers:

**Input Layer:**This is responsible for naturally entering data into the neural network after modeling.

**Output Layer:**This is responsible for issuing the final result of the neural network.

**Hidden Layer:**Between the input layer and the output layer there are a number of layers that convert the input data to the desired output, these layers are called hidden layers.

The word "hidden" implies
that these layers are invisible to external systems and they are
"private" to the neural network.

Some, therefore, refer to this type of
algorithm as the

**“black box”**.
Hidden layers perform calculations
and computations on weighted inputs and produce net inputs which are then
applied to produce actual outputs through an activation function.

There may be zero or more hidden
layers in an artificial neural network.

Each hidden layer contains a certain
number of neurons that receive data from the neurons of the previous layer and
then perform a nonlinear mathematical operation and send it to the next layer,
and so on until they reach the last layer (output layer).

Each neuron in the neural network
has a multiplicative coefficient called "weight" and is used during
the learning process to determine the importance of each feature of the input
data.

Conclusion

The core of the learning process is
to repeat the prediction and then reset the network weights to improve the
results of the prediction process.

The goal is to find the set of
weights that lead to the best possible prediction result.

Therefore, we want to note here that
deep learning models, after the learning process, are only matrices of weights
that are used to convert the input data to the output data by multiplying each
feature of the input data with the weights reached during the learning process.

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