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Introduction to Artificial Neural Networks

Written By: Shekainah Bag-ao

Illustration of the thought processes in the brain. (2015). Retrieved from

Artificial intelligence (AI) is a rapidly growing field in the tech industry and has become increasingly relevant in recent years. At the core of AI is deep learning, which involves the use of an artificial neural network (ANN). In this article, we will explore what an artificial neural network is and how it operates, providing a solid foundation for understanding more advanced AI concepts. This is your headstart toward unraveling the complex world of AI!

What is an Artificial Neural Network?

Artificial neural networks (ANNs) are computer systems that mimic the way the human brain processes information. These networks consist of interconnected processing nodes, known as artificial neurons, arranged in layers. When data is input into the network, it passes through these layers and undergoes a series of mathematical functions until a final output is produced.

Understanding the Structure and Function of Artificial Neural Networks

Bre, F., Gimenez, J., Fachinotti, V. (2017). Artificial neural network architecture [Diagram]. Retrieved from

An ANN has three main layers: the input layer, hidden layer(s), and output layer. The input layer receives data and converts it into numeric vectors or activations, which are passed on to the hidden layer(s). The hidden layer(s) perform the majority of the mathematical computations, extracting and processing data received from the input layer through various functions such as the sigmoid function or the rectified linear unit (ReLU) function. The output layer produces the final output in the form of a numeric vector, representing the predicted class.

In addition to these layers, an ANN also has weights and biases, which are parameters that determine how the input data is calculated by the neurons in the hidden layers. They indicate the importance of the input data, with larger values having a greater impact on the predicted output. ANNs also have an error function, which compares the predicted output to the correct known output to evaluate the accuracy of the network's performance. This process of adjusting weights and biases is called the training process of the ANN.

The Advantages and Limitations of Artificial Neural Networks

Artificial neural networks are used in a wide range of applications, including image and speech recognition, robotics, natural language processing, weather forecasting, stock market prediction, fraud detection, and even in healthcare. With the help of ANNs, processing data has never been easier.

One of the key advantages of ANNs is their ability to analyze and interpret complex and diverse data sets. They can identify patterns and relationships within data that may not be immediately apparent to humans, making them useful for tasks such as image or speech recognition, where the data may be too large or complex for a human to analyze manually.

However, ANNs do have some limitations. They can take a significant amount of time to train, as the algorithms must be fed a large amount of data and fine-tuned to achieve a high level of accuracy. Additionally, ANNs are highly dependent on the quality of the data they are trained on, and if the data is biased or contains errors, this can impact the accuracy of the final output.

Despite these limitations, ANNs are a powerful tool in the field of artificial intelligence and have the potential to revolutionize a wide range of industries. As the technology continues to develop and improve, it is likely that ANNs will play an increasingly important role in the future of AI.

Additional Learning Materials:

To learn more about artificial neural networks (ANNs) and deep learning, check out the following resources:

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