Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.
Explores the history, models, training, convergence, and limitations of neural networks, including the backpropagation algorithm and universal approximation.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.
Delves into challenges of learning from probabilistic models, covering computational complexity, data reconstruction, and statistical gaps.
