Easy, High-Level Introduction
by Pete McCollum Saipan59@juno.com Introduction
Multi-armed bandit
A row of slot machines in Las Vegas. In probability theory, the multi-armed bandit problem (sometimes called the K-[1] or N-armed bandit problem[2]) is a problem in which a gambler at a row of slot machines (sometimes known as "one-armed bandits") has to decide which machines to play, how many times to play each machine and in which order to play them.[3] When played, each machine provides a random reward from a probability distribution specific to that machine. The objective of the gambler is to maximize the sum of rewards earned through a sequence of lever pulls.[4][5]
Towards Reproducible Descriptions of Neuronal Network Models
Introduction Science advances human knowledge through learned discourse based on mutual criticism of ideas and observations. This discourse depends on the unambiguous specification of hypotheses and experimental procedures—otherwise any criticism could be diverted easily. Moreover, communication among scientists will be effective only if a publication evokes in a reader the same ideas as the author had in mind upon writing [1]. Scientific disciplines have over time developed a range of abstract notations, specific terminologies and common practices for describing methods and results. These have lifted scientific discourse from handwaving arguments about sloppily ascertained observations to precise and falsifiable reasoning about facts established at a well-defined level of certainty.
Low-level w/ full algorthms
A single-layer network has severe restrictions: the class of tasks that can be accomplished is very limited. In this chapter we will focus on feed-forward networks with layers of processing units. Minsky and Papert (Minsky & Papert, 1969) showed in 1969 that a two layer feed-forward network can overcome many restrictions, but did not present a solution to the problem of how to adjust the weights from input to hidden units. An answer to this question was presented by Rumelhart, Hinton and Williams in 1986 (Rumelhart, Hinton, & Williams, 1986), and similar solutions appeared to have been published earlier (Werbos, 1974; Parker, 1985; Cun, 1985).
Hyperopt by hyperopt
hyperopt is a Python library for optimizing over awkward search spaces with real-valued, discrete, and conditional dimensions. Currently two algorithms are implemented in hyperopt: Random SearchTree of Parzen Estimators (TPE) Hyperopt has been designed to accommodate Bayesian optimization algorithms based on Gaussian processes and regression trees, but these are not currently implemented.
OCR w/ Ruby
Introduction to Neural Networks The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations. This is particularly useful in applications where the complexity of the data or task makes the design of such a function by hand impractical.
Motivational Theories and Design
This page was originally authored by Diana Bang (2011). This page was added to by Marijke Henschel (February 2013) This page is being edited by Christopher Ward (January-April 2014) Motivation is the force that drives one to act[1]. It involves biological, cognitive, emotional, and/or social factors within a human being or animal that arouse and direct goal-oriented behaviour [2]. It is a construct that cannot be directly observed, and must be inferred from what is perceived to be purposeful behaviour.
Neurons w/ Python
Neurons, as an Extension of the Perceptron Model In a previous post in this series we investigated the Perceptron model for determining whether some data was linearly separable. That is, given a data set where the points are labelled in one of two classes, we were interested in finding a hyperplane that separates the classes.
20 Resources for Teaching Kids How to Program & Code
Isn't it amazing to see a baby or a toddler handle a tablet or a smart phone? They know how technology works. Kids absorb information so fast, languages (spoken or coded) can be learned in a matter of months. Recently there has been a surge of articles and studies emerging about teaching kids to code. We live in a "Back to the Future" movie.
Six degrees of separation
Six degrees of separation. Early conceptions[edit] Shrinking world[edit]
