# What are neural networks and what are their types?

Nowadays, neural networks stand out as one of the most advanced technologies in the field of artificial intelligence and machine learning. This revolutionary technology represents a paradigm shift in how we process data and make decisions based on models that learn from experience.

Neural networks, sometimes called neural networks, are based on a complex architecture inspired by the human brain, aiming to mimic how the human brain processes information.

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## The concept of neural networks

A neural network is a computational model designed to simulate the way the nervous system works in living organisms. The network consists of a group of basic units called neurons, which are connected to each other through connections, similar to nerves in the brain.

Each neuron receives signals from other neurons, processes these signals, and then sends the results to the other neurons. These processes are carried out in specific mathematical ways that allow the model to learn and adapt to new data.

## History of neural networks

The idea of neural networks dates back to the mid-1943th century, when scientists began trying to understand how computer systems could mimic human thinking. The first mathematical model of neural networks was presented in XNUMX by Warren McCulck and Walter Bates, and they laid the foundation for understanding how to design networks that mimic the workings of the brain.

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### Key stations in the development of neural networks

**The fifties and sixties**: Developing the first simple neural network models, such as the Perceptron model presented by Frank Rosenblatt.**The seventies**: The emergence of the problem of weak signals and challenges in training multi-layer neural networks.**1980s**Introducing the backpropagation technique, which solved many training problems and helped improve the performance of neural networks.**XNUMXs**: The use of neural networks has expanded in various fields thanks to improvements in algorithms and increased computing power.**The first decade of the new millennium**: The emergence of recurrent neural networks and convolutional neural networks, which opened the door to new applications in natural language processing and computer vision.

## How neural networks work

Neurons are the basic unit of a neural network, similar in function to neurons in the human brain. Each neuron receives signals from other neurons, processes these signals via a mathematical function, and then sends the result to other neurons.

### Neural network algorithm

Neural network algorithm is one of the most powerful tools in the field of artificial intelligence and machine learning. By combining feed-forward and feed-back, this algorithm can learn complex patterns and make accurate decisions based on the available data.

Whether you work in image classification, word processing, or other applications, neural networks provide a flexible and powerful algorithm to improve performance and achieve outstanding results.

Neural networks consist of several layers:

**Input Layer**: Receives raw data from the external environment.**Hidden Layers**: Data is processed in different ways, depending on the complexity of the model and the number of layers. Each hidden layer contains neurons that take inputs from the previous layer and produce outputs that are sent to the next layer.**Output Layer**: Provides the final result of data processing.

Weights represent the strength of communication between neurons. Each connection between two neurons has a weight that is adjusted during the training process to improve the accuracy of the network.

The activation function determines whether a neuron will activate or not based on the signal it receives. There are several activation functions such as:**Step Function, Sigmoid Function, And the ReLU (Rectified Linear Unit) function.**

### Common loss functions

**Mean Squared Error function**: used to measure the error between actual and predicted values in predictive problems.**Cross-Entropy Loss Function**: It is used in multi-class classification to measure the difference between two probability distributions.

### Weight optimization algorithms

**Stochastic Gradient Descent (SGD) algorithm**: Used to update the weights after each training sample.**Adam's algorithm**: It is used to update the weights in an improved way that combines the advantages of the stochastic gradient algorithm and the adaptive gradient algorithms.

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### Steps of the backpropagation algorithm

Backward learning algorithm is the basic method for training multi-layer neural networks. This algorithm consists of two main steps: **Forward Propagation** و**Backward Propagation**.

#### 1. Forward Propagation

In this step, data is passed through the network from the input layer to the output layer via hidden layers. The outputs at each neuron are calculated using the current activation function and weights.

**Step 1**: Passing the input through the neurons in the first layer and calculating the output using the activation function.**Step 2**: Using the outputs of the first layer as inputs to the next layer, and repeating the process until you reach the output layer.**Step 3**: Calculating the final output of the network.

#### 2. Error Calculation

After obtaining the final output, the error is calculated using a loss function. Error is the difference between expected output (actual output) and actual output (produced by the network).

#### 3. Backward Propagation

In this step, the weights are adjusted to reduce the error calculated in the previous step. This process is done by passing the error from the output layer to the input layer, adjusting the weights at each step.

**Step 1**: Calculating the gradient of error for each neuron in the output layer.**Step 2**: Use gradient to adjust the weights between the output layer and the last hidden layer.**Step 3**: Repeat the process for the hidden layers, calculating the error gradient for each neuron and adjusting the weights using the calculated gradients.**Step 4**: Repeat the process until you reach the input layer.

#### 4. Iteration

The feedforward, error calculation, and feedback steps are repeated for several cycles (Epochs) until the network reaches an acceptable level of accuracy. During this process, the weights are constantly adjusted to improve network performance and reduce error.

## An illustrative example of how neural networks work

Suppose we want to build a neural network to classify images between “cat” and “dog.” We will follow the following steps:

**Data Entry**:- We receive an image as input to the neural network. The image is converted into a set of numbers (pixels).

**First layer (input layer)**:- This layer receives the raw data (pixels) and passes it to the hidden layer.

**Hidden layers**:- Each hidden layer contains neurons that receive signals from the previous layer. These neurons process signals using weights and activation functions, and then send the results to the next layer.
- For example, if an image has 1000 pixels, and the input layer has 1000 neurons, each pixel corresponds to one neuron.

**Output layer**:- Finally, the output layer produces the final result. If the network was designed to classify images into “cat” or “dog,” the output layer would contain two neurons. One represents “cat” and the other “dog.”
- The resulting value from the output layer is used to determine the classification. If the value is higher for a “cat” neuron, the image is classified as “cat,” and vice versa.

### Training process

The process of training a neural network takes place through repeated steps that include:

**Forward Propagation**:- Data from the input layer is passed through the hidden layers to the output layer.

**Error calculation**:- The output produced by the network is compared to the desired output (correct classification). The error is calculated using a loss function.

**Backpropagation**:- The weights are adjusted via a feed-back process to reduce error. The error is distributed from the output layer back to the hidden layers, and the weights are modified in mathematical ways to improve the accuracy of the model.

**Repetition**:- This process is repeated for several cycles (Epochs) until the network reaches an acceptable level of accuracy.

### How to train neural networks

Training neural networks occurs through an iterative process of presenting data, calculating errors, and adjusting weights. Steps include:

**Data Entry**Passing data across the network from the input layer to the output layer.**Error calculation**: Compare expected output with actual output.**Adjust the weights**: Using the return learning algorithm to adjust the weights and reduce the error.**Repetition**: Repeat this process for several cycles until the network reaches an acceptable level of accuracy.

## Types of neural networks

### Simple neural networks

A simple neural network, or perceptron, is a simple model consisting of a single layer of neurons. This model is used for binary classification and is the basis for many complex models.

### Multilayer neural networks

These networks consist of multiple layers of neurons, including an input layer, hidden layers, and an output layer. Hidden layers help handle complex relationships between data, making them suitable for classification and prediction tasks.

### Recurrent neural networks

Recurrent neural networks are used to process sequential data, such as text and speech. It has the ability to retain information from previous courses and use it in subsequent courses, making it ideal for tasks such as machine translation and text analysis.

### Convolutional neural networks

Convolutional neural networks are mainly used in image and video processing. It relies on applying multiple filters to the image to extract features, allowing for highly effective pattern and shape recognition.

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## Simple neural networks

(Perceptron)

A simple neural network is a linear model used for binary classification. It consists of a single neuron or a small group of neurons that takes an input and produces a single output. This model can handle simple classification problems.

### Simple neural network applications

**Text classification**: Determine whether text belongs to a specific category or not.**Spam detection**Classify email as spam or legitimate.**Recognize handwritten numbers**: Identify handwritten numbers through simple pictures.

## Multilayer neural networks

(Multilayer Perceptron)

Multilayer neural networks consist of three main layers:

**Input layer**: Receives raw data.**Hidden layers**: Processes data and extracts features.**Output layer**: Produces the final results.

Each layer consists of neurons connected to the neurons of the previous and subsequent layers through weights. Activation functions are used to convert outputs into usable values.

### Applications of multilayer neural networks

**Image classification**: Recognizing objects in images.**Text analysis**Classification of emotions in texts.**Financial forecasting**: Anticipating financial market trends.

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## Recurrent neural networks

(Recurrent Neural Networks – RNN)

Recurrent neural networks use recurrent connections between neurons, allowing them to store information from previous steps. This makes it capable of processing sequential data effectively.

### Applications of recurrent neural networks in natural language processing

**Machine translation**: Converting texts from one language to another.**Text generation**: Creating new texts based on previous models.**Sentiment analysis**: Identifying emotions from written texts.

## Convolutional neural networks

(Convolutional Neural Networks - CNN)

Convolutional neural networks rely on applying multiple filters to an image to extract important features. It consists of convolutional layers and pooling layers that work together to reduce the size of the data and increase the accuracy of the model.

### Applications of convolutional neural networks in image and video processing

**Face recognition**: Identifying people through their photos.**Video analysis**: Discover activities and events in videos.**Medical Imaging**: Analysis of medical images to diagnose diseases.

## Training and education in neural networks

### Learning algorithms in neural networks

**Supervised learning**: Training the network using data with known results.**Unsupervised learning**: Discovering patterns in data without known outcomes.**Reinforcement learning**: Improving performance through trial and error.

### The role of big data in training neural networks

Big data plays a vital role in improving the accuracy of neural networks. The greater the amount of data available for training, the more capable the network is of learning subtle patterns and making accurate decisions.

## Challenges facing neural networks

Overlearning occurs when a neural network learns fine details of training data rather than general patterns, reducing its ability to generalize to new data. This problem can be reduced by:

**Use regularization techniques**: Such as Dropout and boosting techniques.**Reduce model complexity**: Using fewer neurons or layers.**Increase the size of the training set**: Using more data to train the network.

Lack of data can be a major challenge in training neural networks. To solve this problem you can use:

**Incremental learning techniques**: Create additional data through techniques such as rotation and zoom.**Use pre-trained models**: Start with pre-trained models and modify them to fit your data.**Get additional data**: Through cooperation with other institutions or using open data sources.

## Practical applications of neural networks

### Applications of neural networks in medicine

**diagnoses of illnesses**: Using medical images to analyze and diagnose diseases.**Drug discovery**: Using neural models to discover and develop new drugs.**Gene analysis**: Studying genetic patterns and determining relationships between genes and diseases.

### Applications of neural networks in e-commerce

**Personal recommendations**: Provide personalized product recommendations based on user purchase history and behavior.**Market analysis**: Anticipating market trends and analyzing consumer behavior.**Fraud detection**: Pattern analysis to detect fraudulent activities.

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### Applications of neural networks in modern technology

**Self-driving cars**: Using neural networks to analyze data from sensors and make driving decisions.**Virtual assistants**: Improving the performance of virtual assistants like Siri and Alexa through natural language analysis.**Augmented Reality**: Improving the augmented reality experience through object recognition and environment analysis.

### Applications of neural networks in image and video recognition

Neural networks play a major role in image and video recognition applications. They are used for object detection, face recognition, and scene analysis in photos and videos. Applications such as Google's image recognition and YouTube's video recommendation algorithms rely heavily on convolutional neural networks (CNNs).

### Applications of neural networks in natural language processing (NLP)

Natural language processing includes applications such as machine translation, sentiment analysis, and text generation. Recurrent neural networks (RNN) and short-term memory (LSTM) networks play a crucial role in this field, as they help in understanding the context and meaning of written texts.

### Applications of neural networks in speech recognition

Neural networks are also used in speech recognition applications, where they help convert spoken speech into written text. This includes applications such as virtual assistants (such as Siri and Google Assistant) that rely on speech analysis using neural network models.

### Applications of neural networks in health care

In healthcare, neural networks are used to analyze medical images, diagnose diseases, and develop treatment plans. For example, they can be used to analyze X-ray images, MRIs, and other medical data to diagnose conditions more accurately.

### Applications of neural networks in financial forecasting

Neural networks are also used in financial forecasting, where they help analyze financial data, predict market trends, and provide investment recommendations. These applications rely on machine learning models to analyze patterns and extract valuable information from big data.

### Applications of neural networks in self-driving vehicles

Self-driving vehicles rely heavily on neural networks to process data from sensors, cameras, and other systems to determine location, recognize objects, and make driving decisions. Convolutional neural networks (CNN) and recurrent neural networks (RNN) are used to develop self-driving algorithms.

### Applications of neural networks in games

Neural networks play a role in improving artificial intelligence in games, helping to develop non-player characters (NPCs) that behave in intelligent and interactive ways. Modern games use neural networks to deliver more realistic and challenging gaming experiences.

### Applications of neural networks in fraud detection

In the field of cybersecurity, neural networks are used to detect fraudulent activities and cheating. These models help analyze patterns and identify abnormal behaviors that may indicate fraud.

### Applications of neural networks in human resources

Neural networks are used in human resources to analyze employment records, evaluate performance, and make hiring recommendations. This technology helps improve recruitment, training, and employee development processes.

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## The future of neural networks

### Future innovations in neural networks

**Quantum neural networks**: Using quantum computing to accelerate training and learning processes.**Unsupervised deep learning**: Developing new techniques to learn patterns without the need for data with known results.**Adaptive neural networks**: Designing models capable of adapting to changing data quickly and effectively.

Neural networks will continue to play a pivotal role in the development of artificial intelligence, as they can be used to develop intelligent systems capable of learning and adapting to complex environments, opening the door to new innovations and unprecedented applications in various fields.

## Neural networks and deep learning

Deep learning and neural networks are two related terms in the field of artificial intelligence and machine learning, but there are differences between them:

**Neural Networks:**- They are computational models inspired by how the human brain works. It consists of simple units called neurons that are interconnected in layers.
- There are different types of neural networks, such as artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
- Neural networks are used to solve a variety of problems, including classification, prediction, and pattern recognition.

**Deep Learning:**- It is a branch of machine learning that focuses on the use of deep neural networks, which are networks that contain many layers (they can be dozens or hundreds of layers).
- Deep learning relies on complex neural network structures to learn hierarchical representations of data. Each layer in the deep network extracts more complex features than the previous layer.
- Deep learning is used in advanced applications such as voice recognition, computer vision, natural language processing, and machine translation.

**Briefly:**

- Neural networks are the tool or model that is used in machine learning applications.
- Deep learning is a technique that uses many-layer (deep) neural networks to solve complex problems.

Neural networks can be simple or complex (deep), while deep learning specifically refers to the use of neural networks of great depth.

## Conclusion of my article on neural networks

Neural networks represent a revolution in the field of artificial intelligence, allowing us to understand and process data in ways that were not possible before. By applying appropriate strategies in training and application, we can achieve tremendous achievements in various fields. Future expectations for the development of neural networks indicate continued innovation and progress, making it one of the most important technologies of our current era.

**Sources: towardsdatascience | IBM developer | deepai**