Machine learning and artificial intelligence are two of the most critical technologies expected to transform how we work and live in the next few years. They are designed to analyze and predict the future outcomes of various activities based on data collected in the past. For instance, if you’re an Amazon customer, they might suggest a selection of appropriate movies based on your past purchases.
Despite the term artificial intelligence being used to describe machines capable of learning, most of them require the help of human programming. Data scientists prepare the inputs and select the appropriate variables for predictive analytics. With deep learning, this process can be automated.
What is Deep Learning?
Deep learning is a subset of machine learning that’s focused on improving on its own. Unlike machine learning, which uses a variety of concepts, deep learning focuses on learning through artificial neural networks.
Due to the limitations of computing power, neural networks were initially limited. These networks have developed more sophisticated and faster systems through advancements in Big Data analytics. They have been able to perform various tasks, such as speech recognition and image classification.
Deep learning is mainly composed of multiple layers, which are referred to as artificial neural networks. These networks can perform complex operations, such as abstraction and representation, that make sense of text, images, and sound. Deep learning is regarded as the fastest-growing field of artificial intelligence and is widely used by companies to develop new business models.
How Does It Work?
Like the human brain, neural networks are composed of nodes. Each node is connected to an adjacent layer, and the deeper the network, the more it has. The human brain has thousands of neurons.
The signals traveling between nodes in a neural network are then assigned weights. The heavier the node, the more influence it has on the next layer. The final layer of the network is composed of the inputs, which are then processed by the system. Deep learning systems are usually designed with powerful hardware. They have a large amount of data to process and require complex mathematical calculations.
Due to the complexity of the data collected by deep learning systems, they are usually designed with large amounts of data to process. When processed, these systems can identify the answers to complex questions with binary true or false answers.
A facial recognition program uses a neural network to learn how to identify and recognize different parts of a face, such as the lines and edges. It then trains itself to improve its accuracy over time. The program will better understand the face when it reaches the correct answer.