Uncover Artificial Neural Networks: Why Important, It's Types, Applications, Techniques and Benefits ! Simplify Most Complex Human Brain !!

Abstract:

A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modern drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties.

The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing.

It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.

Keywords:

Artificial Neural Networks, Pattern Recognition, Data Classification, Simulations,  Human Brain, Human Intelligence

Learning Outcomes :

After undergoing this article you will be able to understand the following

1. What's Artificial Neural Networks?

2. What's the purpose of artificial neural network?

3. How artificial neural network structure is constructed?

4. How artificial neural network works?

5. How many types of artificial neural network are there?

6. What's the characteristics of artificial neural network?

7. What's are the models of Artificial Neural Networks?

8. What's the techniques of neural networks?

9. What's the applications of artificial neural network?

10. How artificial neural network is beneficial?

11. What's the limitations of artificial neural network?

12. Conclusions

13. FAQs

References

1. What's Artificial Neural Networks?

An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.

Here are some examples of artificial neural networks: 

 

Perceptron

A simple model that acts as the building block of neural networks. Perceptrons are artificial neurons that simulate biological neurons and are used to solve binary classification problems. 

 

Multilayer perceptron (MLP)

The original form of artificial neural networks, and the most common type used in data analytics. MLPs are feedforward networks that can identify complex relationships between inputs and outputs. 

 

Modular neural network

A network made up of multiple independent neural networks that work on different inputs to complete a larger task. 

 

Autoencoder

An unsupervised machine learning algorithm that copies its inputs to its outputs by compressing the input into a latent-space representation. 

 

Convolutional neural network (CNN)

An advanced type of artificial neural network that differs from regular neural networks in how signals flow between neurons. 

2. What's the purpose of artificial neural network?

A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.

The main purpose of artificial neural network is as follows

Neural networks are important because they can help computers learn and make intelligent decisions, and they can be applied to many real-world situations: 

 

Automation: Neural networks can automate tasks like customer service, data analysis, and image processing. 

 

Improved decision-making: Neural networks can provide insights that help businesses make better decisions. 

 

Increased efficiency: Neural networks can improve business processes by reducing errors and automating tasks. 

 

New products and services: Neural networks can help businesses create new products and services. 

 

Complex problem solving: Neural networks can help people solve complex problems by learning and modeling complex relationships between inputs and outputs. 

 

Fraud detection: Neural networks can be used to detect fraud in areas like credit cards and Medicare. 

 

Financial predictions: Neural networks can be used to make financial predictions for stock prices, currency, and more. 

 

Natural language processing: Neural networks can be used for character and voice recognition. 

 

Medical diagnosis: Neural networks can be used for medical and disease diagnosis. 

 

Neural networks are based on how the human brain functions, and are made up of processing units called nodes that pass data to each other. 

3. How artificial neural network structure is constructed?

Artificial neural networks (ANNs) can have different structures, including: 

 

Modular neural networks

These are made up of multiple subnetworks, or modules. 

 

Multilayer perceptrons (MLPs)

These are ANNs that have many perceptrons and are typically structured with three layers: an input layer, an output layer, and hidden layers in between. 

 

Multilayer feed-forward architecture

This is the most common type of ANN and is designed to sort inputs into categories. 

 

Recurrent neural networks (RNNs)

These are designed to recognize sequential patterns in data to predict future scenarios. 

 

Perceptrons

These are artificial neurons that perform computations to detect patterns in input data. 

 

ANNs are adaptive, meaning they modify themselves as they learn from training. They can be supervised or unsupervised, and supervised learning is when a trained model is validated by a separate test set. 

4. How artificial neural network works?

Artificial neural networks (ANNs) are a type of machine learning algorithm that works similarly to the human brain to help computers learn and process data. ANNs are made up of interconnected nodes, or neurons, that are organized in layers:

Input layer: Receives raw input signals from the outside world

Subsequent layers: Receive output from the previous layer

Output layer: Produces the system's output 

 

Here's how ANNs work: 

 

Input: ANNs receive input signals that reach a threshold using sigmoid functions. 

 

Processing: Each node focuses on one aspect of the problem and shares its findings with other nodes. 

 

Learning: ANNs learn from their mistakes and improve continuously. They update their weights based on the error between the predicted output and the correct output. 

 

Output: The output layer produces the system's output. 

 

ANNs are used in many types of artificial intelligence (AI), including large language models (LLMs), AI image generators, and predictive AI models. 

 

5. How many types of artificial neural network are there?

There are several types of artificial neural networks, including: 

 

Convolutional neural networks (CNN)

A type of advanced neural network that differs from regular neural networks in the flow of signals between neurons. 

 

Recurrent neural networks (RNN)

A type of neural network that can process a sequence of inputs and retain its state while processing the next sequence. 

 

Feedforward neural networks

A type of neural network that consists of layers of neurons that process information in a forward direction. 

 

Multilayer perceptron (MLP)

A type of feed-forward neural network that has a set of processing units called neurons that transform data to get an expected output. 

 

Long short-term memory (LSTM)

A type of neural network that uses feedback connections to enable the processing of sequential data. 

 

Modular neural network

A series of artificial neural networks moderated by an intermediary, where each independent neural network works on separate inputs. 

 

Perceptron

A type of artificial neuron or neural network unit that detects certain input data computations. 

 

Generative adversarial networks (GAN)

A type of AI model that consists of two separate neural networks that are pitted against each other in a game-like scenario. 

 

6. What's the characteristics of artificial neural network?

Artificial neural networks (ANNs) have several characteristics, including: 

 

Structure

ANNs are made up of many interconnected processing elements, called neurons, that perform operations. The structure of an ANN is developed through trial and error, as there is no precise rule that determines it. 

 

Activation functions

These functions define how the weighted sum of inputs is transformed into an output. They are usually the same for all hidden neurons. 

 

Learning

ANNs learn by associating inputs with desired outputs. This is done by changing the synaptic weights of each connection in the network. 

 

Loss functions

These functions calculate the prediction error of the ANN. The loss is used to calculate gradients, which are then used to update the weights. 

 

Network parameters

ANNs have many parameters, or weights, due to the high connectivity between neurons. 

 

Data collection

ANNs require large databases of independent variables and the corresponding dependent variables that need to be predicted. 

 

7. What's are the models of Artificial Neural Networks?

There are many types of artificial neural network models, including: 

 

Recurrent neural network

A model used to predict and model nonlinear systems. 

 

Convolutional neural network

A type of neural network that can react to surrounding units in its coverage area. CNNs are often used to process grid-patterned data, such as images. 

 

Perceptron

A simplified model of biological neurons in the brain that is a building block of artificial neural networks. 

 

Modular neural network

A series of neural networks that work on separate inputs and are moderated by an intermediary. Each neural network acts as a module and completes a sub-task of the network's overall task. 

 

Generative adversarial network (GAN)

A type of AI model that consists of two neural networks that compete against each other in a game-like scenario. The generator network creates fake data that looks real. 

 

Hopfield network

A well-known artificial neural network with a wide range of applications. It's based on the brain and is made up of many neurons connected by self-connection and inter-connection synapses. 

8. What's the techniques of neural networks?

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a machine learning method that uses a network of interconnected nodes to mimic the human brain's structure and function. 

 

Here are some key aspects of neural networks: 

 

Structure

Neural networks are made up of layers of nodes, or artificial neurons, that are connected to each other. These layers include an input layer, an output layer, and one or more hidden layers in between. 

 

Learning

Neural networks learn by being presented with a set of inputs and outputs, and then adjusting their weights and thresholds based on the results. This process is repeated until the network has learned to produce accurate results. 

 

Applications

Neural networks are used in a wide range of applications, including image recognition, natural language processing, and predictive modeling. 

 

Types

There are different types of neural networks, including static, dynamic, and statistical. 

 

History

The history of neural networks includes the "neural network winter" of the 1970s and 1980s when little research was conducted on the topic. 

9. What's the applications of artificial neural network?

Artificial neural networks (ANNs) have many applications, including: 

 

Computer vision: ANNs can analyze images and videos to distinguish and recognize objects, similar to humans. 

 

Speech recognition: ANNs can analyze human speech, even when there are variations in pitch, tone, language, and accent. Speech recognition is used in virtual assistants and automatic transcription software. 

 

Natural language processing (NLP): ANNs can help computers understand the meaning of human-created text. 

 

Recommendation engines: ANNs can track user activity to make personalized recommendations. 

 

Medical applications: ANNs are used in diagnosis, medical image analysis, radiology, and electronic signal analysis. 

 

Solar energy: ANNs are used to model and design solar steam generating plants, and to predict the performance of solar water-heating systems. 

 

Stock market prediction: ANNs can predict stock market indices and stock values by analyzing historical data. 

 

Music composition: ANNs can be used for music composition. 

 

Robot control: ANNs can be used to control robots. 

 

Human action recognition: ANNs can recognize human actions. 

 

10. How artificial neural network is beneficial?

Artificial neural networks (ANNs) are beneficial in many ways, including: 

 

Modeling complex relationships: ANNs can learn and model complex, nonlinear relationships between input and output data. 

 

Predicting outcomes: ANNs can be used to predict how ecosystems respond to environmental changes, or to classify and segment diseases like Alzheimer's and breast cancer. 

 

Image processing: ANNs are used in popular image processing techniques like vector quantization, eigenvector extraction, and 2D filtering. 

 

Spell checking: ANNs are used in text editors to help users identify spelling errors. 

 

Facial recognition: ANNs can be trained to recognize and classify objects in images, such as faces. 

 

Speech-to-text transcription: ANNs can be used to transcribe speech into text. 

 

Weather prediction: ANNs can be used to predict weather. 

 

Fraud identification: ANNs can be used to identify fraud. 

 

ANNs are made up of neurons with different layers that are connected by numeric weights. The weights can be modified as the network learns. 

 

11. What's the limitations of artificial neural network?

Artificial neural networks (ANNs) have several limitations, including: 

 

Black box nature: It can be difficult to understand how ANNs make predictions or categorize data because they are black box AI models. 

 

Computational expense: ANNs are computationally expensive and time-consuming to train because they require a lot of memory and processing power. 

 

Hardware dependency: ANNs require processors with parallel processing capabilities, which makes them dependent on hardware. 

 

Numerical translation: ANNs work with numerical information, so all problems must be translated into numerical values before they can be presented to the ANN. 

 

Inaccurate results: ANNs can produce incomplete or inaccurate results if they are not trained properly. 

 

Overfitting: ANNs are prone to overfitting, especially when trained on small data sets. 

 

Development time: ANNs can take a long time to develop.

12. Conclusions

Neural network is a vast subject. Many data scientists solely focus on only Neural network techniques.

In this session we practiced the introductory concepts only. Neural Networks has much more advanced techniques. There are many algorithms other than back propagation.

Neural networks particularly work well on some particular class of problems like image recognition.

The neural networks algorithms are very calculation intensive. They require highly efficient computing machines. Large datasets take significant amount of runtime on R. We need to try different types of options and packages.

Currently there is a lot of exciting research going on, around neural networks.

After gaining sufficient knowledge in this basic session, you may want to explore reinforced learning, deep learning etc.

13. FAQs

Here are some frequently asked questions about artificial neural networks (ANNs): 

 

What is an ANN?

An ANN is a computational model that mimics the human brain's structure and function. It's made up of interconnected nodes, or artificial neurons, that are organized into layers. ANNs are a type of machine learning that's used to solve complex problems like recognizing faces and summarizing documents. 

 

How do ANNs work?

ANNs learn by adjusting the connection strengths between nodes during training. They use input and output data to learn what factors lead to the output data. ANNs are able to make predictions when fed new input data. 

 

What are the three layers of an ANN?

An ANN has three layers: the input layer, the hidden layers, and the output layer. 

 

How does an ANN learn?

ANNs learn by adjusting the connection strengths between nodes during training. 

 

How are ANNs used in AI?

ANNs are a key component of artificial intelligence (AI). They're used in many types of AI, including large language models (LLMs) like ChatGPT, AI image generators like DALL-E, and predictive AI models. 

 

References

Neural Network books

From sources across the web

Neural Networks and Deep Learning: A Textbook

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Authors: Charu C. Aggarwal

Deep Learning

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Authors: Ian Goodfellow, Aaron Courville, Yoshua Bengio

Deep Learning with Python

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Authors: François Chollet

The Math of Neural Networks

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Authors: Michael Taylor

Neural network design

Neural networks for pattern recognition

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Authors: Christopher Bishop

Neural smithing

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Authors: Russell D. Reed

Machine learning

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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Authors: Geron Aurelien

Deep Learning: Foundations and Concepts

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Authors: Hugh Bishop, Christopher Bishop

The Hundred-page Machine Learning Book

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Authors: Andriy Burkov

The Universal Workflow Of Machine Learning

Artificial intelligence

Back propagation

Deep Learning: A Practitioner's Approach

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Authors: Josh Patterson, Adam Gibson