Deep Learning and Applications I

1 credits · Graduate

Topics include supervised, unsupervised, and reinforcement learning industry tools to develop machine learning systems. Data collection and processing (APIs, web scraping, and Hadoop, MapReduce, Spark), multilayer perceptron (deep neural nets, training deep neural nets, convolutional, neural networks, recurring neural networks, Word2Vec). The course will end with a session on solving practical problems with deep learning.