What are some applications of transformer networks beyond NLP? What are generative adversarial networks (GANs), and how do they learn to generate new data samples?

Principles, techniques, and algorithms in machine learning from the point of view of ... Fundamentals of deep learning, including both theory and applications. Opening the black box of deep neural networks via information. R Shwartz-Ziv, N Tishby. Entropy, 21(219), 3390, 2019. 1836, 2019. Tabular Data: Deep Learning is ... Jun 22, 2018 ... ... learn in comparison with fully connected neural networks. As ... For example, deep learning is frequently used for the classification of ... Mar 9, 2015 ... Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. Recently proposed evidential neural networks ex- plicitly account for different uncertainties by treat- ing the network's outputs as evidence to parame- terize ... Oct 26, 2022 ... In the human brain, each neuron interconnects with another neuron to receive information, process it, and pass it to other neurons. In much the ... Nov 22, 2024 ... However, while some biomarkers are crucial, many lack informative value. To enhance prediction accuracy and understand disease mechanisms, it is ... Jan 28, 2025 ... Learn more about the difference between AI, machine learning, and deep learning ... This content has been made available for informational ... Feb 7, 2024 ... A multi-layer perceptron (MLP) is a type of artificial neural network consisting of multiple layers of neurons. The distinction between narrow and general intelligence is already apparent in the ... of machine learning, most commonly used to refer to deep neural networks.