machine learning

Deep Learning Foundations

Neural Networks   http://www.cs.toronto.edu/~bonner/courses/2014s/csc321/lectures/lec5.pdf DNN: gradient check, learning rate, initialization(not zero, asymmetric), local minimum, plateau momentenm  (exponential average of previous gradients, pointing to the same direction), saddle point() Tips for training Regularization: norm penalties, early stopping, data augmentation, drop out (hidden units cannot co-adapt, generally used, test time:expectation, geometric average for signel layer) Auto encoder:… Continue reading Deep Learning Foundations

machine learning

Kinds of Machine Learning

Kinds of learning: Based on the information available: Supervised learning, Reinforcement learning, Unsupervised learning Based on the role of the learner: Passive learning, Active learning Scenarios: Membership Query Synthesis: the learner may request labels for any unlabeled instance in the input space Stream-Based Selective Sampling: it can first be sampled from the actual distribution, and then… Continue reading Kinds of Machine Learning