Deep learning is a subset of machine learning in artificial intelligence (AI) that is inspired by the structure and functioning of the human brain. It is a branch of AI that is focused on algorithms based around artificial neural networks and is a type of supervised machine learning that uses algorithms to analyze data and define patterns to solve complex problems. Deep learning was first introduced in the early 1980s and has seen its popularity increase gradually since then, leading to its current widespread use.
A neural network is a collection of interconnected nodes (or neurons) that can perform tasks in a fashion that is similar to that of biological neural networks. It is a powerful computer process that involves thousands of nodes which alters their weights and connection strength (similar to how the neuron pathways in the brain are strengthened by repetition). As the network is fed data, it can recognize patterns and make predictions with accuracy that is well beyond the capabilities of basic algorithms.
There are many applications for deep learning, including image classification, speech recognition, natural language processing, object detection, automatic language translation, and facial recognition. In addition to elaborate use cases such as ethical decision-making, autonomous transportation, and medical diagnostics, deep learning can be applied on a smaller scale in day-to-day tasks such as weather forecasting, stock market performance, early detection of diseases, and more.
Deep learning is widely seen as one of the most effective methods of artificial intelligence, and it is rapidly gaining momentum in the ever-changing world of data analysis. It has already surpassed its predecessor machine learning in terms of performance and speed, and it is considered to be the foundation for AI-driven problem-solving solutions of the future.
