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Related lectures (30)

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Explores simulating large-scale neural network models and optimizing memory efficiency in neural simulations using NEURON and CoreNEURON.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Explores the optimization of computational physics calculations and the relationship between physicists and computers.

Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.

Covers the basics of computing, human computers, Von Neumann architecture, and memory units.

Covers programming concepts for engineers, including interactive quizzes and practical applications to enhance learning and engagement.

Explores the relationship between neuroscience and machine learning in visual intelligence.

Explores orchestration graphs for enhancing learning with simulations and the importance of consolidating knowledge through practice.

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