Neuroscience and ML

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Related lectures (32)

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Learning & Memory

Explores learning, memory, cognitive loads, metacognition, and problem-solving tasks.

Memory & Learning

Explores memory, learning, cognitive load, and problem-solving strategies to enhance learning and cognitive performance.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Modeling the Role of Hippocampus in Memory

Delves into the critical role of the hippocampus in memory formation, focusing on declarative memory and spatial representation.

Memory Encoding and Neuroplasticity

Explores memory encoding, neuronal plasticity, types of memory, brain regions, and clinical cases.

Neuroscience and Machine Learning

Explores the intricate relationship between neuroscience and machine learning, highlighting the challenges of analyzing neural data and the role of machine learning tools.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Deep Learning for Autonomous Vehicles: Learning

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

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Statistical Physics in Machine Learning: Understanding Deep Learning

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