In this course, we learn more on extensions. and prior knowledge. We want to minimize the number of hidden nodes(=> less overfitting) The approaches: start with a large networks and prune. start with a small networks and grow. There are two example. Let eac…
Let's continue talk about overtraining. The number of training set samples should be much larger than the number of weights roughly for \(N^2\)(N = the number of weights). minimize the number of nodes and layers(weights) noise injection and noise to the…
in order to decide by how much of the result blamed or credited to a particular weight. We need sigmoid function. Sigmoid function = any S-shape function. examples. logistical function \( y=f(s)=\frac{1}{1+e^{- \lambda s}} \), \(\lambda\) decides slope. When \…
This course, I learnt basic pattern recognition , perceptron and an overview of how to train/adjust the perceptron. Here are some basic terms we should know. pattern recognition = feature extraction + classification Feature extraction = find "good" f…
A question, what is AI? Artificial Intelligence: It is not the art of making computers behave as they do in the movies. It is motivated by the brain, but do not overdo it. We always regard it as a black box. After opening this box, we can see a network of simp…
I am going to record my course notes for a basic machine learning course, Natural Computation Methods for Machine Learning ( 1DL073 ). There are some course notes and personal understanding. I will not show full slides and provide the e-book of literature. How…
