What are Neural Networks?
The following is an excerpt from Andrei Oprisan's book Understanding Machine Learning: Approaches, Algorithms, and Business Applications.
Neural networks are machine learning algorithms modeled after the structure and function of the human brain. Neural networks consist of layers of interconnected nodes designed to recognize patterns in data. Each node in a neural network performs a simple mathematical operation on its inputs and passes the result to the next layer of nodes. The output of the last layer is the final prediction of the neural network.
Neural networks can be used for tasks such as image recognition, speech recognition, and natural language processing. In image recognition, a neural network can be trained to recognize specific objects in images, such as faces, buildings, or animals. In speech recognition, a neural network can remember spoken words and convert them to text. Finally, a neural network can generate human-like responses to text-based inputs in natural language processing.
Neural networks can be trained using supervised, unsupervised, or a combination of the two. In supervised learning, the network is trained on labeled data, where the correct output is known for each input. The network adjusts its weights and biases to minimize the difference between its predicted output and the correct output. In unsupervised learning, the network is trained on unlabeled data, where the correct output is unknown. The network adjusts its weights and biases to identify patterns in the data.
One of the most popular types of a neural networks is the convolutional neural network (CNN), which is particularly effective for image and video recognition tasks. CNNs use a combination of convolutional layers, which identify local patterns in the input, and pooling layers, which reduce the spatial dimensions of the information. The output of the last layer is typically passed through one or more fully connected layers, which perform a final classification or regression task.
Another popular type of neural network is the recurrent neural network (RNN), which is particularly effective for sequence data, such as time series or natural language data. RNNs use a feedback loop to allow information to persist across multiple time steps. This makes them well-suited for speech recognition, machine translation, and text generation tasks.
Neural networks have many real-world applications, particularly in computer vision, speech recognition, and natural language processing. For example, neural networks are used in autonomous vehicles to recognize road signs and traffic lights and in healthcare to diagnose medical images. They are also used in virtual assistants, such as Siri and Alexa, to identify and respond to natural language queries.
Neural networks are modeled after the structure and function of the human brain and can be used for tasks such as image recognition, speech recognition, and natural language processing. Neural networks can be trained using supervised, unsupervised, or a combination of the two. They have many real-world applications in different sectors, such as healthcare, finance, and retail.