Deep learning is a subfield of machine learning that involves building and training neural networks with multiple layers to learn and make predictions on complex data. The term “deep” refers to the fact that these networks have many layers, typically ranging from a few to dozens or even hundreds of layers.
Deep learning is particularly well-suited for tasks that involve large amounts of data, such as image and speech recognition, natural language processing, and autonomous driving. By using multiple layers, deep neural networks can learn hierarchical representations of the data, where each layer captures increasingly complex features or patterns.
The training process in deep learning involves using a large dataset to optimize the weights and biases of the neural network through a process called backpropagation. During training, the model makes predictions on the input data, and the difference between the predicted output and the true output is used to adjust the weights and biases in the network.
Some popular deep learning architectures include convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequence modeling, and transformers for natural language processing. Deep learning has revolutionized many fields, including computer vision, speech recognition, and natural language processing, and is a rapidly growing area of research and development in the field of AI.