Deep Learning has significantly advanced due to increased data availability and computational power, finding applications across sectors such as speech and image recognition and online advertising. It has surpassed human performance in specific tasks, notably in image recognition, driving its widespread adoption in businesses. Key techniques in Deep Learning include Multilayer Perceptrons (MLPs), which are used for image identification and spam detection; Convolutional Neural Networks (CNNs), known for their effectiveness in image classification; Recurrent Neural Networks (RNNs), which are adept at time-series analysis and Natural Language Processing; Long Short Term Memory Networks (LSTMs), which excel in learning long-term dependencies; Generative Adversarial Networks (GANs), used for creating realistic images and media; and Restricted Boltzmann Machines (RBMs), which are useful for tasks like classification and feature learning. Each technique has unique attributes and applications, and the ongoing evolution of deep learning continues to encourage its integration into various fields.