A neural network involves taking a dataset to analyze relationships through multiple and different algorithms. Some of these algorithms include using inputs, biases, activation functions. There are three different types of neural networks that include fast-feeding neural networks, convolutional neural networks, and recurrent neural networks. These neural networks act as every other model such that there are ways to modify it. This includes but not limited to, regularization, adjusting features, etc. Neural Networks are relatively new technology that is still in the works of development just like Natural Language Processing (NLPs).
Every day, neural networks are being used without us even knowing it. For example, the face-ID that is used when we unlock our phones utilizes convolutional neural networks or CNNs. Even before the invention of face-ID, CNNs were used also for fingerprint detection on our phones and other security locations. CNNs are most utilized for processing image data. There are many features on our faces that include the eyes, nose, and mouth that are reflected as data and stored on our phones. Every time we unlock our phones, our facial data is taken to ensure that the model is accurate before unlocking our phones. In airport security, neural networks have revolutionized safety by using image processing data to detect contrabands. This involves taking data from thousands of bodies who have been caught with contrabands. The dataset involves different features such as BMI, clothing types, the type of contraband, and the number of contrabands itself. This model can help predict the probability that a specific passenger who is passing through security will be in position of a contraband.
