Deep learning is a subfield of artificial intelligence (AI) that uses algorithms inspired by the structure and function of the brain, called neural networks, to process and analyze large amounts of data. Its goal is to enable computers to learn from experience and perform tasks that typically require human-level intelligence, such as recognizing patterns, making predictions, and classifying data. It is called "deep" because the algorithms have multiple layers that process and analyze the data, allowing for complex decision-making and problem-solving.
When a deep learning algorithm is trained on a large dataset, it adjusts the connections between the nodes to minimize the difference between the output it produces and the actual results. This process is repeated many times until the algorithm produces the desired output.
For example, if a DL algorithm is trained on a dataset of images of dogs and cats, it will learn to identify the features that differentiate the two types of animals, such as the shape of their eyes, nose, and ears. Once trained, the algorithm can then be used to classify new images as either a dog or a cat. This process of adjusting the connections between the nodes to produce the desired output is what allows deep learning algorithms to learn from the data and improve their performance over time.
