This course surveys computation by models of networks of neurons, covering design and training of feedforward and recurrent neural networks, supervised and unsupervised learning methods, and biologically constrained algorithms, while examining issues such as generalizability and adversarial inputs.
This course surveys computation by models of networks of neurons, covering design and training of feedforward and recurrent neural networks, supervised and unsupervised learning methods, and biologically constrained algorithms, while examining issues such as generalizability and adversarial inputs.
