Neurorobotics: Sensory Representations and Learning Principles

Related lectures (32)

Page 3 of 4

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Explores two-layer neural networks and backpropagation for learning feature spaces and approximating continuous functions.

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

Explores atom mapping in chemical reactions and the transition to reaction grammar using the transformer architecture.

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

Covers synaptic plasticity, types of synapses, Hebb's postulate, LTP, LTD, and network oscillations in the hippocampus.

Delves into the continual learning of representational models in brain intelligence, emphasizing rapid adaptation to unstructured environments.

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.