Introduction to Neural Networks
9.641J · Brain and Cognitive Sciences, Physics · Graduate · Spring 2005
Prof. Sebastian Seung
This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.
The syllabus, on MIT OpenCourseWare
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Attribution
Prof. Sebastian Seung. 9.641J Introduction to Neural Networks. Spring 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: CC BY-NC-SA 4.0.
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