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• medium.com/onfido-tech/machine-learning-101-be2e0a86c96a

  Machine Learning 101. A brief introduction to machinea| | by Peter Roelants | Onfido Tech | MediumThe optimisation algorithm tries to find the best combination of parameters so that given the example x the model’s output y is as close to the expected output as possible. If we now want to create a model that best approximates target t for given input x f..

• videolectures.net/mlas06_pittsburgh

  Autumn School 2006: Machine Learning over Text and Images - Pittsburgh - VideoLectures - VideoLectures.NETIt is intended for students who already have a familiarity with machine learning, and is designed for software developers, graduate students, and advanced researchers with an interest in learning more about this area. I am glad that it is intended for stude..

• blog.spion.dev/posts/machine-learning-ethics.html

  Machine learning ethicsMore generally, a value function is given by the ML system creator that measures the desireability of a certain outcome or behaviour (it could include multiple things like number of product bought, number of ads clicked or viewed, etc). You end up with a an..

• slideshare.net:443/polyvore/mturk-machine-learning-8417481

  MTurk > Machine LearningThe Turk (circa 1770) Invented in 1770 by Mr.Wolfgang.The first “machine” that could play chess.Beat challengers like Napoleon and Benjamin Franklin.Hoax revealed 50 years later.12wikipedia.com Mturk > ML if (proble..

• ibm.com/downloads/cas/GB8ZMQZ3

  Machine Learning for Dummies

• medium.com/xeneta/boosting-sales-with-machine-learning-fbcf2e618be3

  Boosting Sales With Machine Learning | by Per Harald Borgen | Xeneta | MediumIn this blog post I’ll explain how we’re making our sales process at Xeneta more effective by training a machine learning algorithm to predict the quality of our leads based upon their company descriptions. In essence, Xeneta help companies that ship contai..

• zinkov.com/posts/2012-10-05-datamining-software

  Machine Learning SoftwarePatsy isn’t quite a machine learning library, but it very good for designing features to then be used in conjuction with something like scikit-learn. Built on top of numpy and scipy, this library offers a wide variety of machine learning algorithms and a ve..

• ai.mit.edu/courses/6.867-f04/lectures.html

  6.867 Machine LearningMon 9/20||Lecture 4: statistical regression, uncertainty, active learning| Wed 9/29||Lecture 7: support vector machines, kernels||Notes on Lagrange multipliers (postscript)| Mon 10/25||Lecture 12: VC-bounds, structural risk minimization, compression and mod..

• heatonresearch.com/encog

  Encog Machine Learning Framework | Heaton ResearchEncog is a pure-Java/C# machine learning framework that I created back in 2008 to support genetic programming, NEAT/HyperNEAT, and other neural network technologies. Encog supports a variety of advanced algorithms, as well as support classes to normalize an..

• blog.bigml.com/2018/02/23/kicking-off-the-bigml-internship-program

  Machine Learning IntershipsFeb 2018 - We are excited to launch our Internship Program, which welcomes advanced students and young professionals to join the BigML Team. The BigML platform continues to expand and improve, thanks to our top-notch team that has been international since day one. Dur..

• people.cs.umass.edu/~lrn

  Machine Learning LaboratoryDue to the death of Professor Paul Utgoff in 2008, this machine learning Laboratory is no longer operating. In the UMass machine learning Laboratory (directed by Prof. Utgoff), we study computational methods that enable machines to learn from instruction. T..

• jeremykun.com/2012/08/04/machine-learning-introduction

  Machine Learning -- Introduction - Math ProgrammingAug 2012 - While we do intend to cover the classical topics of machine learning, such as neural networks and decision trees, we would like to quickly approach more sophisticated modern techniques such as support vector machines and methods based on Kolmogorov complexi..

• papers.labml.ai/paper/2106.08962

  Machine Learning PapersWe present and motivate the problem of efficiency in deep learning, followed by a thorough survey of the five core areas of model efficiency (spanning modeling techniques, infrastructure, and hardware) and the seminal work there. We believe this is the firs..

• telezia.com

  Machine Learning Decision Support - Machine Learning ApplicationHelp computers “learn” from your data. Integration Solutions · Artificial Intelligence · Cognitive Computing. Artificial intelligence in healthcare uses algorithms to approximate human cognition in the analysis of complex medical data.

• phys.org/news/2017-09-quantum-machines.html

  Quantum machine learningThus, in recent years, quantum machine learning has become a matter of interest because of is vast potential as a possible solution to these unresolvable challenges and quantum computers show to be the right tool for its solution. They introduce quantum mac..

• springer.com/journal/10994

  Machine Learning | HomeThe journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems. Reports substantive results on a wide range of learning methods applied to a variety of learning problems. Call for P..

• betterexplained.com/articles/adept-machine-learning-course

  Studying a Course (Machine Learning) with the ADEPT Method - BetterExplainedI've read a smattering of blog posts, the subject is growing, and after my friend asked me to join the class, I had to sign up. machine Learning: Create models with Linear Algebra, then improve them with Calculus. This course is among the most fun I've had ..

• web.archive.org/web/20131011010744/daoudclarke.github.io/machine%20learning%20in%20practice/2013/10/08/machine-learning-libraries

  Machine Learning LibrariesOct 2013 - After wonderful feedback on my previous post on Scikit-learn from the guys at /r/MachineLearning, I decided to collect the list of machine learning libraries into this seperate note. Scikit-learn: comprehensive and easy to use, I wrote a whole article on wh..

• akosiorek.github.io/ml/2020/08/12/machine_learning_of_sets.html

  Machine Learning of SetsAug 2020 - where \(p_c(k)\) is the probability mass function of the set size, \(k!\) accounts for all possible permutations of set elements \(\mathbf{x}_i\), \(U \in \mathbb{R}_+\) is the unit volume expressed as a scalar value, and \(f_k\) is the permutation-invarian..

• web.archive.org/web/20200804203352/codingossip.github.io/2020/test-first-machine-learning

  Test-First Machine Learning | codingossipAug 2020 - When values are mocked, success conditions are defined and tests are running, it’s time to show that the ML model under testing is training and performing predictions. As in traditional software engineering, testing precise execution behavior, timing or per..