Use Westlaw legal research when being wrong is not an option. With Thomson Reuters Westlaw, you'll find legal information you need quickly, confidently, and know your research is complete using the world's most preferred online legal research service.

Use Westlaw legal research when being wrong is not an option. With Thomson Reuters Westlaw, you'll find legal information you need quickly, confidently, and know your research is complete using the world's most preferred online legal research service.

Use Westlaw legal research when being wrong is not an option. With Thomson Reuters Westlaw, you'll find legal information you need quickly, confidently, and know your research is complete using the world's most preferred online legal research service.

Use Westlaw legal research when being wrong is not an option. With Thomson Reuters Westlaw, you'll find legal information you need quickly, confidently, and know your research is complete using the world's most preferred online legal research service.

Where we go one, we go all.

As of Monday, there were 115 people confirmed dead, according to Maui police.

As of Monday, there were 115 people confirmed dead, according to Maui police.

Hawaii officials urge families of people missing after deadly fires to give DNA samples. https://t.co/MPSs5ajvbo pic.twitter.com/FYFxabJc3s— FOX 12 Oregon (@fox12oregon) August 23, 2023

==Welcome to Q Research General=='''We are researchers who deal in open-source information, reasoned argument, and dank memes. We do battle in the sphere of ideas and ideas only. We neither need nor condone the use of force in our work here.''''' "We hold these truths to be self-evident: that all men are created equal; that they are endowed by their Creator with certain unalienable rights; that among these are life, liberty, and the pursuit of happiness." ''==VINCIT OMNIA VERITAS | SEMPER FIDELIS | WWG1WGA | QRESEARCH====Q's Latest Posts==>>18284019 Nov. - Dec. 2022==Q's Private Board==>>>/projectdcomms/ & '''Q's Trip-code:''' Q !!Hs1Jq13jV6==Find Q drops here==Qresear.ch/q-posts, Qanon.pub, Qalerts.pub, OperationQ.pub. Qposts.online, 8kun.top/qresearch/, Qalerts.app, Qalerts.net, douknowq.com/134295/Q-Anon-Pub.htm==Q Posts Archives=='''* Q Map & Mirrors PDF:''' SCRIBD: https:''//''www.scribd.com/document/419874308/Q-Anon-The-Storm-X-VII?secret_password=55SQ1tCYhuNR8ESzm50u'''* Q Posts Archive, Searchable, interactive with user-explanations:''' qanon.pub qanon.app'''* Q Posts Archive + RSS, Searchable, Analytics, Offsite Bread Archive:''' qanon.news'''* Q Raw Text Dumps:''' q-clock.com/q_raw.txt'''* Q Original, full-size images Q has posted:''' https:''//''postimg.cc/gallery/29wdmgyze/'''* Q Research Memo & OIG Report Links:''' 8kun.top/qresearch/res/426641.html#427188'''* Q Research Notables:''' Archive Board >>>/qnotables/==Q Research Board==Key Resources below; ck Dough Resource thread for more: >>17225239'''New to QR?'''>>17240320, >>17238117 Intro, Board Info, Offsite Bunkers, Optics>>17238430 Suggested Follow | >>17240363 Information Tools & Services'''Threads'''>>17253611 Dedicated resource threads>>17242392 Q Encyclopedia by ArchiveAnon | >>17242386 Q Video Archive by ArchiveAnon'''Join Us'''>>17322518 E-BAKE instructions | >>17322509 LEARN TO BAKE>>17240554 Meme Requests | LEARN MEME WARFARE https:''//''archive.is/2iZX1'''TOR Access'''__TOR URL__: http:''//''w7m432cocr665kf5tlpcxojwldajr3njd2etcxwhpbrt44eemuxhp7ad.onion/qresearch/catalog.html__TOR Banner__: https:''//''www.youtube.com/watch?v=MLCupx1UExg

==Welcome to Q Research General=='''We are researchers who deal in open-source information, reasoned argument, and dank memes. We do battle in the sphere of ideas and ideas only. We neither need nor condone the use of force in our work here.''''' "We hold these truths to be self-evident: that all men are created equal; that they are endowed by their Creator with certain unalienable rights; that among these are life, liberty, and the pursuit of happiness." ''==VINCIT OMNIA VERITAS | SEMPER FIDELIS | WWG1WGA | QRESEARCH====Q's Latest Posts==>>18284019 Nov. - Dec. 2022==Q's Private Board==>>>/projectdcomms/ & '''Q's Trip-code:''' Q !!Hs1Jq13jV6==Find Q drops here==Qresear.ch/q-posts, Qanon.pub, Qalerts.pub, OperationQ.pub. Qposts.online, 8kun.top/qresearch/, Qalerts.app, Qalerts.net, douknowq.com/134295/Q-Anon-Pub.htm==Q Posts Archives=='''* Q Map & Mirrors PDF:''' SCRIBD: https:''//''www.scribd.com/document/419874308/Q-Anon-The-Storm-X-VII?secret_password=55SQ1tCYhuNR8ESzm50u'''* Q Posts Archive, Searchable, interactive with user-explanations:''' qanon.pub qanon.app'''* Q Posts Archive + RSS, Searchable, Analytics, Offsite Bread Archive:''' qanon.news'''* Q Raw Text Dumps:''' q-clock.com/q_raw.txt'''* Q Original, full-size images Q has posted:''' https:''//''postimg.cc/gallery/29wdmgyze/'''* Q Research Memo & OIG Report Links:''' 8kun.top/qresearch/res/426641.html#427188'''* Q Research Notables:''' Archive Board >>>/qnotables/==Q Research Board==Key Resources below; ck Dough Resource thread for more: >>17225239'''New to QR?'''>>17240320, >>17238117 Intro, Board Info, Offsite Bunkers, Optics>>17238430 Suggested Follow | >>17240363 Information Tools & Services'''Threads'''>>17253611 Dedicated resource threads>>17242392 Q Encyclopedia by ArchiveAnon | >>17242386 Q Video Archive by ArchiveAnon'''Join Us'''>>17322518 E-BAKE instructions | >>17322509 LEARN TO BAKE>>17240554 Meme Requests | LEARN MEME WARFARE https:''//''archive.is/2iZX1'''TOR Access'''__TOR URL__: http:''//''w7m432cocr665kf5tlpcxojwldajr3njd2etcxwhpbrt44eemuxhp7ad.onion/qresearch/catalog.html__TOR Banner__: https:''//''www.youtube.com/watch?v=MLCupx1UExg

Satellites are at the center of how we communicate with technology, and it’s our job to defend them from internal and external threats.

test of neuroevoultion with cars that learn to drive a track Newest Video with Simplified NN : https://www.youtube.com/watch?v=DgRUyE7UJ3o Support: https://www.patreon.com/miorsoft Web: https://miorsoft.github.io/Site/index.html

Intelligent User Interfaces is where HCI meet Artificial Intelligence. Applications will be in interactive machine learning (iML).

Assignment 3 for the Machine Learning for Health Informatics course at TU Vienna - Anonymization using the SaNGreeA algorithm - GitHub - cassinius/mlhi-ass2-anonymization: Assignment 3 for the Mach...

The main idea behind this project is to bring in the Human into a machine learning algorithm. In this setup the Travelling salesman problem is chosen.

Human behavior is marked by a sophisticated ability to attribute outcomes and events to choices and experiences with surprising nuance. Understanding the mechanisms that govern this ability is a major focus for cognitive neuroscience. Reinforcement learning (RL) theory has provided a tractable

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.

'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines.

Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.

PyData London 2016 Probabilistic programming is a new paradigm that greatly increases the number of people who can successfully build statistical models and machine learning algorithms, and makes experts radically more effective. This talk will provide an overview of PyMC3, a new probabilistic programming package for Python featuring intuitive syntax and next-generation sampling algorithms. Machine learning is the driving force behind many recent revolutions in data science. Comprehensive libraries provide the data scientist with many turnkey algorithms that have very weak assumptions on the actual distribution of the data being modeled. While this blackbox property makes machine learning algorithms applicable to a wide range of problems, it also limits the amount of insight that can be gained by applying them. The field of statistics on the other hand often approaches problems individually and hand-tailors statistical models to specific problems. To perform inference on these models, however, is often mathematically very challenging, and thus requires time-deriving equations as well as simplifying assumptions (like the normality assumption) to make inference mathematically tractable. Probabilistic programming is a new programming paradigm that provides the best of both worlds and revolutionizes the field of machine learning. Recent methodological advances in sampling algorithms like Markov Chain Monte Carlo (MCMC), as well as huge increases in processing power, allow for almost complete automation of the inference process. Probabilistic programming thus greatly increases the number of people who can successfully build statistical models and machine learning algorithms, and makes experts radically more effective. Data scientists can create complex generative Bayesian models tailored to the structure of the data and specific problem at hand, but without the burden of mathematical tractability or limitations due to mathematical simplifications. This talk will provide an overview of PyMC3, a new probabilistic programming package for Python featuring intuitive syntax and next-generation sampling algorithms. ---- PyData is a gathering of users and developers of data analysis tools in Python. The goals are to provide Python enthusiasts a place to share ideas and learn from each other about how best to apply our language and tools to ever-evolving challenges in the vast realm of data management, processing, analytics, and visualization. We aim to be an accessible, community-driven conference, with tutorials for novices, advanced topical workshops for practitioners, and opportunities for package developers and users to meet in person. www.pydata.org Notebook: https://gist.github.com/anonymous/9287a213fe188a79d7d7774eef79ad4d Slides: https://docs.google.com/presentation/d/1QNxSjDHJbFL7vFwQHHheeGmBHEJAo39j28xdObFY6Eo/edit Twitter: https://twitter.com/twiecki 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps