Deep learning algorithms mimic the human brain using neural networks to process data hierarchically. They consist of layers of interconnected nodes (neurons), each performing mathematical computations on input data.
The network learns by adjusting weights and biases through a process called backpropagation, which minimizes error by iteratively updating parameters using gradient descent. Layers closer to the input learn basic features, while deeper layers capture complex patterns.
These algorithms excel in handling large datasets and solving problems like image recognition, natural language processing, and speech synthesis.