Non-Conceptual Knowledge Systems

Related lectures (32)

Page 1 of 4

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.