Deep learning frameworks are software libraries designed to simplify the process of building and training deep learning models. They provide pre-built functions and tools that allow developers to create complex neural networks without needing to write extensive code from scratch. Popular frameworks include TensorFlow, PyTorch, and Keras, each offering unique features and capabilities. These frameworks support various tasks, such as image recognition, natural language processing, and reinforcement learning. By leveraging these tools, researchers and developers can accelerate their work, experiment with different architectures, and deploy models more efficiently in real-world applications.