background preloader

Machine Learning Cheat Sheet (for scikit-learn)

Machine Learning Cheat Sheet (for scikit-learn)
As you hopefully have heard, we at scikit-learn are doing a user survey (which is still open by the way). One of the requests there was to provide some sort of flow chart on how to do machine learning. As this is clearly impossible, I went to work straight away. This is the result: [edit2] clarification: With ensemble classifiers and ensemble regressors I mean random forests, extremely randomized trees, gradient boosted trees, and the soon-to-be-come weight boosted trees (adaboost). [/edit2] Needless to say, this sheet is completely authoritative. Thanks to Rob Zinkov for pointing out an error in one yes/no decision. More seriously: this is actually my work flow / train of thoughts whenever I try to solve a new problem. Only that I always start out with "just looking". Anyhow, enjoy ;) [edit3] You can find the SVG and dia file I used here.

Related:  Machine learningfregnier

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.

machine learning in Python "We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]." "For these tasks, we relied on the excellent scikit-learn package for Python." "The great benefit of scikit-learn is its fast learning curve [...]" 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]

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. 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.

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.

Rhizomatic Learning - The community is the curriculum Doing this course I've put together a blog post to give you a sense of 'where' the course is happening and what you might like to do as part of it. READ THIS FIRST = Your unguided tour of Rhizo14 Why might this course be for you? Rhizomatic learning is a story of how we can learn in a world of abundance – abundance of perspective, of information and of connection. Education, post-structuralism and the rise of the machines I was asked by the excellent Sheryl Nussbaum-Beach to speak to her PLP class about MOOCs, and, while we had what i thought was an excellent forty minute chat, there were tons of comments that i never had the chance to address. As i look over the questions they asked, I see that in answering their questions i have a chance to lay out many of the thoughts that I have had about MOOCs while they have been all the rage here on the internet in the last few weeks. I opened the discussion with a quick personal intro to my contribution to the MOOC discussion and then we moved to Q & A. Feel free to skim along and pick up the part of the discussion that interests you.

Deep learning Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.[1][2][3][4][5][6] Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.[9]

Semantic Web W3C's Semantic Web logo The Semantic Web is a collaborative movement led by international standards body the World Wide Web Consortium (W3C).[1] The standard promotes common data formats on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web, dominated by unstructured and semi-structured documents into a "web of data". The Semantic Web stack builds on the W3C's Resource Description Framework (RDF).[2] According to the W3C, "The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries