Neural network

A neural network, often called an artificial neural network (ANN), is a type of machine-learning model inspired by the human brain. It's designed to simulate the behavior of biological neurons, creating a system that can learn and improve from experience. This structure makes it capable of solving complex computational problems that are difficult or impossible to solve using traditional programming techniques.

A neural network consists of interconnected layers of nodes, or "neurons." Each connection between neurons carries a certain weight, which adjusts as the network learns from experience. Stronger connections have larger weights. 

Learning typically involves using a method such as gradient descent to minimize a loss function, which measures the difference between the network's output and the expected output

How does a neural network work?

Neural networks are modeled after the human brain, in which neurons are constantly firing to transfer information. A neural network uses a system of nodes, or neurons, to communicate with one another to process and learn from input data.

The process begins at the input layer, where each neuron represents a single feature from the dataset. This could be a pixel from an image, a word from a text, a note from a sound, and so on. The input layer receives raw data and passes it to the next layer: the hidden layer.

Each neuron in a hidden layer receives inputs from multiple neurons in the previous layer. These inputs are multiplied by the weights—parameters that the network learns during training—associated with their connections.

The final layer is the output layer, which produces the result. The output can be a single node for binary classification problems or multiple nodes for multi-class problems

Types of neural networks

There are different types of neural networks designed to solve various problems with big data. Here are some of the most common:

• Feed-forward neural networks: These are the simplest type of artificial neural network. Information in FNNs travels only in one direction—from the input layer, through any hidden layers, to the output layer.
• Convolutional neural networks: Information from different layers in CNNs can be interconnected, unlike in FNNs. CNNs are mainly used for image processing, video processing, and natural language processing. Key components include convolutional layers and pooling layers, which help reduce computational complexity and overfitting.
• Recurrent neural networks: RNNs have connections that form directed cycles. This creates a form of internal memory that helps them remember previous inputs in the sequence, making them excellent for modeling sequential data.
• Long short-term memory: LSTM is a type of RNN that can learn and remember over long sequences and is not prone to the "vanishing gradient" problem in which information disappears as neurons travel. LSTMs are particularly useful for understanding context in documents, text generation, and even music composition.
• Autoencoders: This is a type of artificial neural network used for learning efficient codings of input data. They are typically good for anomaly detection, denoising data, or dimensionality reduction.
• Generative adversarial networks: GANs consist of two parts: a generator network and a discriminator network. The generator tries to create data that's similar to the training data, while the discriminator tries to distinguish between real and fake data.

What do people use neural networks for?

Neural networks are used for a variety of applications. Their ability to learn and make predictions or decisions based on data is beneficial within numerous industries.

One of the most common uses of neural networks is in the field of image recognition. They are used to identify objects, people, and even handwritten or printed text in an image. For example, Google Photos uses a neural network to recognize and categorize your photos based on the people and things in them.

Neural networks are also used in natural language processing. They help machines understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. Siri and Alexa both use them to understand and respond to user requests

The difference between deep learning and neural networks

Deep learning is a type of advanced machine learning that uses large amounts of data and multiple layers to get progressively "smarter," whether it's identifying images more accurately or producing more natural-sounding writing. 

Neural networks provide the basis for deep learning by forming many layers of neurons that communicate to solve complex problems. Deep learning is a process, and it uses neural networks to achieve output.

A single-layer neural network can solve simple machine-learning problems, like linear regression or binary classification. On the other hand, deep learning is a further subset of neural networks. The "deep" in deep learning refers to the presence of multiple layers in a neural network. While a traditional neural network may have one or two hidden layers, a deep neural network can have hundreds or even thousands of layers.