What is Deep Learning?

Deep learning algorithms are usually trained on large sets of labeled data , where they learn to map features in the data to the correct labels. For instance, in an image recognition task, the algorithm may learn to associate specific features of an image such as an object’s shape or color with the appropriate label, like “dog” or “cat.” After training, the algorithm can effectively classify new, unseen data by applying the patterns it has learned.

Deep learning functions through artificial neural networks that learn directly from data. These networks consist of layers of interconnected nodes, with each node responsible for detecting a particular feature. For example, in an image recognition network, the first layer may identify edges, the next layer may recognize shapes, and a higher layer may detect complete objects.

During learning, the weights of the connections between nodes are adjusted to improve the network’s ability to classify data. These adjustments are guided by the network’s performance on training data, typically using methods such as backpropagation. This entire process is known as training and can be carried out using different approaches, including supervised learning, unsupervised learning, and reinforcement learning.

Both machine learning and deep learning are subfields of artificial intelligence, with machine learning serving as the broader category that includes deep learning as one of its approaches. Depending on the task and algorithm, both can be trained on labeled or unlabeled data .

They are widely applied to areas such as image recognition, speech recognition, and natural language processing. However, deep learning often surpasses traditional machine learning in handling complex pattern recognition tasks such as image classification and object detection because of its ability to learn hierarchical representations of data.