Explores the history, models, training, convergence, and limitations of neural networks, including the backpropagation algorithm and universal approximation.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
Covers spontaneous brain network activity, neural simulation, and validation, emphasizing the importance of in-vitro and in-vivo conditions for accurate network modeling.
Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.
