Surely you are familiar with the view above, right? Yup , it’s true that there is a display from ChatGPT which was booming a few months ago. However, do you know what technology is used in ChatGPT? That’s right, basically ChatGPT is a chatbot that utilizes deep learning technology . It is one of the sub-fields of machine learning that utilizes an algorithm called Artificial Neural Network.
Hm , is this the first time you’ve heard of the algorithm? Congratulations, you are in the right place because in this article, we will get acquainted with the Artificial Neural Network algorithm. Well, as usual we will start with the definition first, shall we?
What are Artificial Neural Networks?
Simply put, Artificial Neural Network (ANN) is an algorithm or mathematical model inspired by the workings of the nervous system in the human brain in processing information. ANN generally consists of a collection of artificial neurons (artificial neurons) spread over several layers (layers) as shown below.
Artificial Neural Networks
So, as shown above, an ANN model usually has several layers, namely the input layer , the hidden layer , and the output layer . The input layer Hungary Mobile Number List receives input data that is used to train the model, while the output layer produces output which is the predicted result of the ANN. The hidden layer is located between the input and output layers, and has the function of processing the input and producing an internal representation of the data.
The processes that occur in the hidden layer and the output layer are carried out by the smallest unit in the ANN called a neuron. It will receive input from external (from the input layer) or other neurons, and produce an output. The output will then be processed by the next neuron to produce a new output. This process will continue until the last layer (output layer). Well, the output of this last layer is considered as the predicted result of the ANN model.
Artificial Neural Network prediction results
The prediction results from the ANN model will then be evaluated by calculating the value of the loss function . It is a metric to determine how well the prediction results produced by the model. Next, we carry out the optimization process based CRYP Email List on the value of the loss function by utilizing various optimizer algorithms, such as gradient descent , Adam optimizer , etc. The purpose of this evaluation and optimization process is to produce better predictive outputs. If you want to learn more about ANN, you can read about it in the Learning Machine Learning for Beginners class on the Dicoding platform .
Application of ANNs
Okay, we already know what ANN is. Now it’s time for us to see various examples of using ANN in real life.
Well, actually ANN has been widely applied in various fields, such as finance, health, industry, etc. Here are some examples of its application.
Image Classification One
of the most common applications of ANN is image classification. In this case we use the ANN model to study the patterns contained in the image. This pattern will be used to classify an image into a certain category.
Disease Detection Disease detection is one application of the ANN model for the health sector. ANN can study data patterns from sick and healthy patients, then it can be used to assist health workers in diagnosing a disease.