Linear regression deep learning is a technique that uses a neural network to perform linear regression on a dataset. In machine learning, linear regression is a common method used to predict a continuous output variable based on one or more input variables. Deep learning involves using neural networks with many layers to learn complex relationships in the data. In linear regression deep learning, the input variables are fed into the neural network, which learns to map them to the output variable by adjusting the weights and biases of the network. The neural network can be trained using techniques such as gradient descent or backpropagation to minimize the error between the predicted output and the actual output. Once the model is trained, it can be used to predict the output for new input variables. Linear regression deep learning is commonly used for applications such as stock price prediction, weather forecasting, or medical diagnosis.