DL (Deep Learning)

Information

• Deep Learning is a subset of machine learning that uses neural networks with multiple hidden layers to learn and extract features from large amounts of data.
• The architecture of a deep neural network typically consists of several layers of neurons, each performing a different transformation on the input data.
• Deep learning models are capable of learning hierarchical representations of data, which can capture complex patterns and relationships between the input features.
• Deep learning has revolutionized many fields such as image recognition, natural language processing, speech recognition, and more.
• Deep learning models require a large amount of data and computing resources to train. They are typically trained using backpropagation and stochastic gradient descent to adjust the weights of the network.
• Some of the popular deep learning architectures include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).
• Deep learning has also been applied to a wide range of energy-related problems such as energy forecasting, load prediction, fault detection, and energy management.