Last week, as part of one of my PhD courses, I gave a one hour seminar covering one of the machine learning tools which I have used extensively in my research: neural networks. Preparation of the seminar was very useful for me, since it required me to make sure that I really understood how the networks function, and I (think I) finally got my head around back-propagation – more on that later. In this post, and depending on length, the next (few), I intend to reinterpret my seminar into something which might be of use to you, dear reader. Here goes!
A neural network is a method in the field of machine learning. This field aims to build predictive models to help solve complex tasks by exposing a flexible system to a large amount of data. The system is then allowed to learn by itself how to best form its predictions. Continue reading “Understanding Neural-Networks: Part I”