For more info : DEEP LEARNING.compressed Reference : (//www.coursera.org/course/neuralnets) Presenter : Juno This seminar introduces Deep learning, which has been a buzz word in machine learning area for recent years. Since it is based on neural network, the seminar first introduces the basic theory and history of Neural network and explains why Neural network failed in 1990s and early 2000s. Unsupervised way of feature learning, is then followed to describe the core of Deep learning. The latter half of the seminar focuses on the algorithms that learn features from data in an unsupervised way. As a result, main algorithms such as Hopfield Net, Boltzmann machine, and Restricted Boltzmann machine will be discussed.
We had an AKA AI seminar last Friday. We had our AI centers in the US and South Korea come together and Andy lead the seminar. The summary of the seminar is as follows: 1. RBM RBM stands for Restricted Boltzmann Machine. It can be seen as a system in DBNs (Deep Belief Networks), which are multilayered nerve networks in an MLP form. We should understand the following concepts in order to understand DBNs and RBM. i. MLP (Multilayer Perceptron) ii. Stochastic Modeling iii. EBM (Energy Based Model) All three of the concepts mentioned above are of importance in machine learning and constructing theoretical backgrounds for DBNs and RBM. However, the three are basically independent from each other. Short explanations […]