MIT Introduction to Deep Learning

Talk Abstract: In spite of great success of deep learning a question remains to what extent the computational properties of deep neural networks (DNNs) are similar to those of the human brain. The particularly non-biological aspect of deep learning is the supervised training process with the backpropagation algorithm, which requires massive amounts of labeled data, and a non-local learning rule for changing the synapse strengths. In this talk I will describes a learning algorithm that does not suffer from these two problems. It learns the weights of the lower layer of neural networks in a completely unsupervised fashion. The entire algorithm utilizes local learning rules which have conceptual biological plausibility.