An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 317303 Data School
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-312357973/m-438108645 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 94078 Udacity
Get access to practice questions, written summaries, and homework help on our website! http://wwww.simplelearningpro.com Follow us on Instagram http://www.instagram.com/simplelearningpro Like us on Facebook http://www.facebook.com/simplelearningpro Follow us on Twitter http://www.twitter.com/simplelearningp If you found this video helpful, please subscribe, share it with your friends and give this video a thumbs up!
Views: 320813 Simple Learning Pro
This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 174227 Udacity
What is SURVIVORSHIP BIAS? What does SURVIVORSHIP BIAS mean? SURVIVORSHIP BIAS meaning - SURVIVORSHIP BIAS definition - SURVIVORSHIP BIAS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Survivorship bias, or survival bias, is the logical error of concentrating on the people or things that "survived" some process and inadvertently overlooking those that did not because of their lack of visibility. This can lead to false conclusions in several different ways. The survivors may be actual people, as in a medical study, or could be companies or research subjects or applicants for a job, or anything that must make it past some selection process to be considered further. Survivorship bias can lead to overly optimistic beliefs because failures are ignored, such as when companies that no longer exist are excluded from analyses of financial performance. It can also lead to the false belief that the successes in a group have some special property, rather than just coincidence (Correlation proves Causation). For example, if three of the five students with the best college grades went to the same high school, that can lead one to believe that the high school must offer an excellent education. This could be true, but the question cannot be answered without looking at the grades of all the other students from that high school, not just the ones who "survived" the top-five selection process. Survivorship bias is a type of selection bias. In finance, survivorship bias is the tendency for failed companies to be excluded from performance studies because they no longer exist. It often causes the results of studies to skew higher because only companies which were successful enough to survive until the end of the period are included. For example, a mutual fund company's selection of funds today will include only those that are successful now. Many losing funds are closed and merged into other funds to hide poor performance. In theory, 90% of extant funds could truthfully claim to have performance in the first quartile of their peers, if the peer group includes funds that have closed. In 1996, Elton, Gruber, and Blake showed that survivorship bias is larger in the small-fund sector than in large mutual funds (presumably because small funds have a high probability of folding). They estimate the size of the bias across the U.S. mutual fund industry as 0.9% per annum, where the bias is defined and measured as: "Bias is defined as average ? for surviving funds minus average ? for all funds" (Where ? is the risk-adjusted return over the S&P 500. This is the standard measure of mutual fund out-performance). Additionally, in quantitative backtesting of market performance or other characteristics, survivorship bias is the use of a current index membership set rather than using the actual constituent changes over time. Consider a backtest to 1990 to find the average performance (total return) of S&P 500 members who have paid dividends within the previous year. To use the current 500 members only and create a historical equity line of the total return of the companies that met the criteria, would be adding survivorship bias to the results. S&P maintains an index of healthy companies, removing companies that no longer meet their criteria as a representative of the large-cap U.S. stock market. Companies that had healthy growth on their way to inclusion in the S&P 500, would be counted as if they were in the index during that growth period, when they were not. Instead there may have been another company in the index that was losing market capitalization and was destined for the S&P 600 Small-cap Index, that was later removed and would not be counted in the results. Using the actual membership of the index, applying entry and exit dates to gain the appropriate return during inclusion in the index, would allow for a bias-free output.
Views: 506 The Audiopedia
Bridging disciplines in analysing text as social and cultural data workshop (21-22 September, 2017) The potential benefits of using large-scale text data to study social and cultural phenomena is increasingly being recognized, but researchers are currently scattered across a range of often distinct research communities. However, many methodological challenges cut across research disciplines and require interdisciplinary synergies. This workshop aims to address the gap between research methodologies in NLP/ML and the humanities and the social sciences. More information here: https://dongpng.github.io/attached/
Views: 301 The Alan Turing Institute
Presented by Garret Christensen of the Berkeley Initiative for Transparency in the Social Sciences.
Views: 1012 UC Davis Social Sciences
View full lesson: http://ed.ted.com/lessons/what-s-the-difference-between-accuracy-and-precision-matt-anticole When we measure things, most people are only worried about how accurate, or how close to the actual value, they are. Looking at the process of measurement more carefully, you will see that there is another important consideration: precision. Matt Anticole explains what exactly precision is and how can help us to measure things better. Lesson by Matt Anticole, animation by Anton Bogaty.
Views: 2864640 TED-Ed
Cylurian step-by-STEP is online - http://cylurian.com A histogram is related to a bar graph and is meant for quantitative data (number data only instead of word data). A histogram is constructed by drawing a rectangle for each bin. Bins are intervals of equal width that cover all the values that were observed in the x-axis.
Views: 248204 cylurian
Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/e/variance?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/v/variance-of-a-population?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/box-and-whisker-plots/v/range-and-mid-range?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1325655 Khan Academy
This video describes five common methods of sampling in data collection. Each has a helpful diagrammatic representation. You might like to read my blog: https://creativemaths.net/blog/
Views: 791886 Dr Nic's Maths and Stats
Mar 17, 2009 during world war ii, american military personnel noticed that some parts of planes were hit by enemy fire more often t"What Is A Selection Bias? Watch more videos for more knowledge Selection Bias - YouTube https://www.youtube.com/watch/Bn25u4XhYq4 Selection Bias - YouTube https://www.youtube.com/watch/iSKerlu3Pr0 Selection Bias: A Real World Example - YouTube https://www.youtube.com/watch/p52Nep7CBdQ What is SELF-SELECTION BIAS? - YouTube https://www.youtube.com/watch/XJmKR8x0wKU Self-selection bias - YouTube https://www.youtube.com/watch/wFrX5wolqP8 What Is A Selection Bias? - YouTube https://www.youtube.com/watch/Om8ZSxUhnCQ Selection bias in case-control studies - YouTube https://www.youtube.com/watch/oG027wOWJHA Bias in Sample Selection - YouTube https://www.youtube.com/watch/3zc_t1MwrPw What are the different types of BIAS? - YouTube https://www.youtube.com/watch/bDDGomrRwA4 Bias in Basic Statistics - YouTube https://www.youtube.com/watch/5K1Hg-pSY1A Selection bias as viewed as a problem with ... https://www.youtube.com/watch/s691VU9oFjc Healthy Worker Bias - YouTube https://www.youtube.com/watch/0bhsDbIrW24 Misclassification bias - YouTube https://www.youtube.com/watch/pPyZgeQK24U Random assignment - removes selection bias - YouTube https://www.youtube.com/watch/pE6zYPjFh4A Threats To Internal Validity - Selection Biases - Nerd ... https://www.youtube.com/watch/d1MKMlHi-uc What is PARTICIPATION BIAS? What does ... https://www.youtube.com/watch/GP7X4cXzYX4 Contextual bias – why nothing exists in isolation - YouTube https://www.youtube.com/watch/6DSVtO03Bek Understanding publication bias (03:09 min) - YouTube https://www.youtube.com/watch/z6E4vljXrNU Understanding unconscious bias - YouTube https://www.youtube.com/watch/dVp9Z5k0dEE USMLE Step 1 Epidemiology Principles: Bias - YouTube https://www.youtube.com/watch/a6dlA8kgmQM" mpling group to be selected selection bias is a kind of that occurs when the researcher decides who going studied. Beware the dangers of selection bias harvard business reviewpsychology glossary identifying and avoiding in research ncbi nih. Edu otlt mph ep bias ep713_bias_print. This statistics glossary includes definitions of all technical terms used definition selection bias statistical error that causes a in the sampling portion an experiment. Fem selection bias and cohort studiesbias statistical analysis handbook. Selection bias definition stat trek. The bias exists due to a flaw in the sample selection process, where subset of aug 18, 2014. What is selection bias? (and how to defeat it) imotions. With observational studies such as cohort, case control and cross sectional studies) selection bias is a distortion in measure of association (such risk ratio) due to sample that does not accurately reflect the target population type caused by choosing non random data for statistical analysis. Selection bias wikipediaselection wikipedia. Selection bias sph sphweb. Googleusercontent search. Definition of selection bias nci dictionary cancer terms & survivorship. It is sometimes referred to as the selection effect definition of bias, from stat trek dictionary statistical terms and concepts. Selection bias a real world example youtube. The ideal study population is clearly defined, accessible, reliable, and at increased risk to jan 1, 2011 self selection bias the problem that very often results when survey respondents are allowed decide entirely for themselves whether or not may occur in cohort studies if exposed unexposed groups truly comparable , e. Selection bias the skeptic's dictionary skepdic. Comparing an occupational cohort with the selection bias is a systematic error in study that occurs from process used to identify (select) participants, allocate them groups and term bias, statistical context, has variety of meanings. They analyzed the bullet holes in returning planes and launched a program to have these areas reinforced so that they could withstand enemy psychology definition for selection bias normal everyday language, edited by psychologists, professors leading students. Html url? Q webcache. Institute for work & healthsample selection bias investopedia. Selection bias wikipedia. Ideally, the subjects in a study should be very jul 29, 2013 explanation of what is bias statisticsarticles on ap statistics and elementary statistics, videos nov 8, 2016 good research begins well before first experiment starts. Help us get better selection bias may occur during identification of the study population. How does selection bias interfere with good research, and how can we prevent it? . What is selection bias? Definition and meaning businessdictionary bias. Self selection bias sage r
Views: 144 Question Tray
Non-spatial, non-temporal * space or time are not relevant to the analysis * sampling can be in space or over time but these are not considered relevant Spatial * location of observations is important for analysis, e.g., mapping Temporal * time that observations are made is important for analysis, e.g., monitoring Spatio-temporal * both space and time are relevant for analysis, e.g., mapping trends over time We begin with non-spatial, non-temporal.
Views: 60 Muhammad Ijaz Anjum Mughal
MDS (multi-dimensional scaling) and PCoA (principal coordinate analysis) are very, very similar to PCA (principal component analysis). There really only one small difference, but that difference means you need to know what you're doing if you're going to use MDS effectively. This video make sure you learn what you need to know to use MDS and PCoA. There is a minor error at 4:14: The difference for gene 3 should be (2.2 - 1)². Instead the distance for gene 2 was repeated. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Views: 27070 StatQuest with Josh Starmer
understanding how the input flows to the output in back propagation neural network with the calculation of values in the network. the example is taken from below link refer this https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ for full example
Views: 164882 Naveen Kumar
Trying to publish in journals That have a high impact But quite often, in fact Methods are far from intact With a p-hack we see that We lack a priori testing But in a system where novelty Is viewed as the best thing Validity flies out the door As we test moderators and more Lose sight of reality Fearing a boot through the door Fearing being that person Who has a low H-index Because their analyses were clean With stats morals like Windex Stuck yielding null findings Not sticking to the binding Conditions that define Any hypothesis any time Maybe delete this outlier Only one, just to try it And unknowingly succumb To sheer confirmation bias Or one can start hypothesizing After results are known Just to make a novel claim That in reality is overblown Like a fisherman in the sea One can reel for false positives Uncover a novel discovery When in reality it's the opposite Searching data like it's a gold mine Trying to earn a good merit Need a p less than .05 Cause it's publish or perish But together we can revise Conventional norms and systems As a field we can rise Toward methodological wisdom We need greater transparency To beat this replication crisis To overcome biases that emerge When left to our own opaque devices Revision starts with new visions That ignite new ignitions Preregistration will reveal All those post hoc decisions
Views: 776 Daniel Rosenfeld
The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the population
Views: 151999 Manager Sahab
Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm types of sampling types of sampling pdf probability sampling types of sampling in hindi random sampling cluster sampling non probability sampling systematic sampling
Views: 388708 Examrace
[http://bit.ly/overfit] When building a learning algorithm, we want it to work well on the future data, not on the training data. Many algorithms will make perfect predictions on the training data, but perform poorly on the future data. This is known as overfitting. In this video we provide formal definitions of over-fitting and under-fitting and give examples for classification and regression tasks.
Views: 28622 Victor Lavrenko
In this video you will learn how to measure whether the Regression model really fits your data well. You will also learn why to use test error to measure model fitness For all our videos & study packs visits: http://analyticuniversity.com/
Views: 17939 Analytics University
This short video gives an explanation of the concept of confidence intervals, with helpful diagrams and examples. Find out more on Statistics Learning Centre: http://statslc.com or to see more of our videos: https://wp.me/p24HeL-u6
Views: 785398 Dr Nic's Maths and Stats
John Oliver discusses how and why media outlets so often report untrue or incomplete information as science. Connect with Last Week Tonight online... Subscribe to the Last Week Tonight YouTube channel for more almost news as it almost happens: www.youtube.com/user/LastWeekTonight Find Last Week Tonight on Facebook like your mom would: http://Facebook.com/LastWeekTonight Follow us on Twitter for news about jokes and jokes about news: http://Twitter.com/LastWeekTonight Visit our official site for all that other stuff at once: http://www.hbo.com/lastweektonight
Views: 14908117 LastWeekTonight
Two examples of articles reviewed using the McMaster Critical Review data extraction tools
Views: 783 Psychological health in the OT workplace
So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 440782 CrashCourse
Relatively simple data science experiments can yield major insights and have a significant impact. Many experiments in data science are expensive and time consuming to pursue. But Latanya Sweeney, professor of government and technology at Harvard University, has shown that even relatively simple studies conducted by students can have a significant impact on public policy and society. As a student in the 1990s, Sweeney discovered that by applying a couple of filters to a database containing supposedly anonymized health records of Massachusetts state employees, she was able to identify the medical history of Gov. William Weld. That simple experiment led to a broader conclusion: Most people in the United States are the only ones in their ZIP code with a particular date of birth, which means it is relatively easy to discover their identities in much the same way Sweeney found Weld’s history. “That impact -- the ability to have a simple experiment and have dramatic impact – was huge, and something that stayed with me forever. That simple experiment was quoted in the preamble of HIPAA and the rewrite of privacy laws around the world,” Sweeney said during a talk at this year’s Women in Data Science (WiDS) conference at Stanford University. Over the years, Sweeney and her associates have flagged numerous instances of flaws in public databases that have caused significant harm. And they’ve found instances where data sources have been misused or applied in a discriminatory manner. A query by a reporter prompted Sweeney to look for a correlation between names typically given to African-Americans and online ads mentioning arrest records. She found it. Online searches containing a name that sounds like it belongs to a black person were 80 percent more likely to generate an ad mentioning arrest records than searches for stereotypically white names. “Somebody goes online to see what they can find out about you and Googles your name. And if the ads are popping up implying that you have an arrest record, then in fact, you’re at a disadvantage. It’s not about the intent or whether it was intended,” said Sweeney, who was chief technology officer for the Federal Trade Commission from January 2014 until December 2014. A study by Sweeney’s students found that a major SAT tutoring company charged higher prices to Asians. And Airbnb modified its pricing policies after the students found price discrimination against certain groups. Referring to the examples she gave during her talk, Sweeney said: “I like to think I’m really smart, but the truth is these are really simple experiments. But they have profound impact because they empower someone else to be able to do their job better.”
Views: 21773 Stanford University School of Engineering
AI expert Joanna Bryson posits that giving artificial intelligence the same rights a human has could result in pretty dire consequences... because AI has already proven that it can pick up negative human characteristics if those characteristics are in the data. Therefore, it's not crazy at all to think that AI could scan every YouTube comment in one afternoon and pick up all the negativity we've unloaded there. If it's already proven it's not only capable of making the wrong decision but eventually will make the wrong decision when it comes to data mining and implementation, why even give it the same powers as us in the first place? Read more at BigThink.com: http://bigthink.com/videos/joanna-bryson-why-creating-an-ai-that-has-free-will-would-be-a-huge-mistake Follow Big Think here: YouTube: http://goo.gl/CPTsV5 Facebook: https://www.facebook.com/BigThinkdotcom Twitter: https://twitter.com/bigthink Joanna Bryson: First of all there’s the whole question about why is it that we in the first place assume that we have obligations towards robots? So we think that if something is intelligent, then that’s their special source, that’s why we have moral obligations. And why do we think that? Because most of our moral obligations, the most important thing to us is each other. So basically morality and ethics are the way that we maintain human society, including by doing things like keeping the environment okay, you know, making it so we can live. So, one of the way we characterize ourselves is as intelligent, and so when we then see something else and say, “Oh it’s more intelligent, well then maybe it needs even more protection.” In AI we call that kind of reasoning heuristic reasoning: it’s a good guess that will probably get you pretty far, but it isn’t necessarily true. I mean, again, how you define the term “intelligent” will vary. If you mean by “intelligent” a moral agent, you know, something that’s responsible for its actions, well then, of course, intelligence implies moral agency. When will we know for sure that we need to worry about robots? Well, there’s a lot of questions there, but consciousness is another one of those words. The word I like to use is “moral patient”. It’s a technical term that the philosophers came up with, and it means, exactly, something that we are obliged to take care of. So now we can have this conversation. If you just mean “conscious means moral patient”, then it’s no great assumption to say “well then, if it’s conscious then we need to take care of it”. But it’s way more cool if you can say, “Does consciousness necessitate moral patiency?” And then we can sit down and say, “well, it depends what you mean by consciousness.” People use consciousness to mean a lot of different things. So one of the things that we did last year, which was pretty cool, the headlines, because we were replicating some psychology stuff about implicit bias—actually the best one is something like “Scientists Show That A.I. Is Sexist and Racist, and It’s Our Fault,” which that’s pretty accurate, because it really is about picking things up from our society. Anyway, the point was, so here is an AI system that is so human-like that it’s picked up our prejudices and whatever… and it’s just vectors! It’s not an ape. It’s not going to take over the world. It’s not going to do anything, it’s just a representation; it’s like a photograph. We can’t trust our intuitions about these things. We give things rights because that’s the best way we can find to handle very complicated situations. And the things that we give rights are basically people. I mean some people argue about animals, but technically, and again this depends on whose technical definition you use, but technically rights are usually things that come with responsibilities and that you can defend in a court of law.
Views: 18493 Big Think
Let's discuss the math behind back-propagation. We'll go over the 3 terms from Calculus you need to understand it (derivatives, partial derivatives, and the chain rule and implement it programmatically. Code for this video: https://github.com/llSourcell/how_to_do_math_for_deep_learning Please Subscribe! And like. And comment. That's what keeps me going. I've used this code in a previous video. I had to keep the code as simple as possible in order to add on these mathematical explanations and keep it at around 5 minutes. More Learning resources: https://mihaiv.wordpress.com/2010/02/08/backpropagation-algorithm/ http://outlace.com/Computational-Graph/ http://briandolhansky.com/blog/2013/9/27/artificial-neural-networks-backpropagation-part-4 https://jeremykun.com/2012/12/09/neural-networks-and-backpropagation/ https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Forgot to add my patron shoutout at the end so special thanks to Patrons Tim Jiang, HG Oh, Hoang, Advait Shinde, Vijay Daniel & Umesh Rangasamy Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 162957 Siraj Raval
This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/v/mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/interpreting-histograms?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan AcademyÂÃÂªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1929150 Khan Academy
A talk on "P-Hacking" by the pseudo-anonymous blogger Neuroskeptic. Summary: "P-Hacking" is a popular tool for extracting positive results from negative data. It's so easy, you can even do it unconsciously. But how does p-hacking work and why is it so popular? In this talk, Neuroskeptic discusses some of the top ways of hacking data, and shows the power of p-hacking by means of a live demonstration. Neuroskeptic also discusses how p-hacking can be detected and prevented.
Views: 8865 TARG Bristol
Did you know that the most intelligent people are usually the least happy individuals among us? If you can relate to most of the signs of a smart person we’ve put together, you must have a high IQ, but might be rather unhappy in life. Follow professional advice of leading experts in psychology to transform your life in a positive way. People with a high IQ continuously analyze the events of their lives. Their findings are often full of fear and disappointment, with real dilemmas constantly popping up in their heads. The habit of constant overanalyzing leads to frequent reflections about life, death and the meaning of existence. All this, in most cases, leads to depression. Try to distract yourself from having negative thoughts and focus on the positive. You can also try keeping a diary and writing about the things you’re thankful for every single day. Anytime you’re feeling down, just open it, and it'll put a smile on your face. According to scientists, socializing for highly intelligent people is often a more painful experience than being alone. Find ways to make new acquaintances whose company you genuinely enjoy and appreciate family members who sincerely love you. A recent study has shown that, in their daily life, intellectuals make just as many mistakes as everyone else! Scientist Igor Grossman of the University of Waterloo suggests that such people should talk about their problems in the third person in order to emotionally distance themselves. This reduces bias and allows them to reach the most logical conclusions. The assumption that every smart person is successful couldn’t be more inaccurate. In fact, studies have shown that 85% of financial well-being depends things such as individuality and the ability to communicate and negotiate. No big brain or certain IQ number on that list.
Views: 151 Psychological Tips
Listen to Dr. Lauren Cohen’s, L.E. Simmons Professor at Harvard Business School, interview from QuantCon NYC 2018. Dr. Cohen’s research expertise is in behavioral finance, studying what makes assets priced the way they are and how they can often be mispriced. He particularly focuses on what drives investor behavior and how that behavior can move prices in different directions. In this interview he shares details on his recent project called “Lazy Prices”. To learn more about Quantopian, visit http://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 3181 Quantopian
In this video you will learn about different sampling techniques used in building predictive model A Population is a collection of observation about which we would like to make an inference Sampling units are nonoverlapping collections of elements from the population Probability Samples – Each member of the population has a non zero probability of getting selected in the sample Examples : Random Sampling, Systematic Sampling & Stratified sampling Non Probability Samples- Members are selected from the population in non random way Convenience sampling, Judgement sampling, Quota Sampling, & Snowball Sampling big edu FB :https://www.facebook.com/Big-Edu-284756735274540/?ref=aymt_homepage_panel
Views: 192 Big Edu
Antisocial behavior and misinformation are increasingly prevalent online. As users interact with one another on social platforms, negative interactions can cascade, resulting in complex changes in behavior that are difficult to predict. My research introduces computational methods for explaining the causes of such negative behavior and for predicting its spread in online communities. It complements data mining with crowdsourcing, which enables both large-scale analysis that is ecologically valid and experiments that establish causality. First, in contrast to past literature which has characterized trolling as confined to a vocal, antisocial minority, I instead demonstrate that ordinary individuals, under the right circumstances, can become trolls, and that this behavior can percolate and escalate through a community. Second, despite prior work arguing that such behavioral and informational cascades are fundamentally unpredictable, I demonstrate how their future growth can be reliably predicted. Through revealing the mechanisms of antisocial behavior online, my work explores a future where systems can better mediate interpersonal interactions and instead promote the spread of positive norms in communities. . . . . . . . . . . . . . . Justin Cheng is a Ph.D. candidate in computer science at Stanford University, where he is advised by Jure Leskovec and Michael Bernstein. His research is at the intersection of data science and human-computer interaction, and focuses on cascading behavior in social networks. This work has received a best paper award, as well as several best paper nominations at CHI, CSCW, ICWSM, and WWW. He is also a recipient of a Microsoft Research Ph.D. fellowship and a Stanford Graduate Fellowship. More information: https://www.ischool.berkeley.edu/events/2017/antisocial-computing-explaining-and-predicting-negative-behavior-online
Views: 448 Berkeley School of Information
2nd NBER Economics of Artificial intelligence Conference Toronto Canada September 2018
Views: 465 Creative Destruction Lab
User interaction data is at the heart of interactive machine learning systems (IMLSs), such as voice-activated digital assistants, e-commerce destinations, news content hubs, and movie streaming portals. In my talk, I will show how we can improve machine learning in such systems through a principled treatment of biases in interaction data via causal inference and counterfactual learning, and through interface interventions that increase the quality and quantity of interaction data from users. All these efforts are part of my larger vision that improving machine learning accuracy in IMLSs is not only a question of improving machine learning algorithms, but that there are also numerous other crucial questions, such as how interfaces affect interaction data quality and quantity. See more at https://www.microsoft.com/en-us/research/video/improving-machine-learning-beyond-the-algorithm/
Views: 1437 Microsoft Research
Description: Ignoring statistical information in favor of using irrelevant information, that one incorrectly believes to be relevant, to make a judgment. This usually stems from the irrational belief that statistics don’t apply to a situation, for one reason or another when, in fact, they do. Also known as: neglecting base rates, base rate neglect, prosecutor's fallacy
Views: 502 Logical Fallacies
►►SentiSquare introduction: ►What we do? To meet the needs of data intensive industries and deliver cost-effective NLP, we offer our AI-based self-learning solution to any text analytics. ►How it works? It is language agnostic, applicable thus to any language without limitations and even in multi-language datasets. Compared to alternatives, where search content is predefined (pre-tagged) our disruptive technology genuinely reflects the actual meaning of text, being not limited by lexicons or biased by content predefinition. ►Distributional Semantics The unique qualitative advantage of our semantic engine derives from the principle known as Distributional Hypothesis. The meaning of words can be induced by statistical comparison of their contexts. Developed from theoretical roots in psychology, linguistics and lexicography, the resulting AI area is referred to as Distributional Semantics. ►Vectorial representation This approach enables the vectorial representation of word meaning. Every word, sentence, or full text are associated with a vector of real numbers which reflects the contextual (distributional) information across a text dataset. Vectorial representation allows us to quantify the similarity between meanings. On this basis, our algorithms are employed to automatically discover hidden patterns. In stark contrast to our competition, which often struggles to expand language capabilities beyond a few widely used languages, we have the ability to build a functional semantic model for any new language in the span of a week. ►Who we are? Our company was created in August 2014 to accomplish the transfer of knowledge from the past 12 years of research of commercially attractive NLP algorithms at the University of West Bohemia, by our expert founders. The unique knowledge of sentiment analysis, semantics and summarization creates the ambition to become a world leader in opinion analytics solutions. The research in Distributional Semantics that our developers have carried out is what powers our unique technology. Strategically, we are continuing to build products and business model around our these unique elements. ►► www.sentisquare.com https://www.linkedin.com/company/sentisquare https://www.facebook.com/sentisquare
Views: 52 SentiSquare
Kriil Tsemekhman, Chief Data Officer at Integral Ad Science, describes his personal journey to causality in advertising and unveils Integral Ad Science’s proprietary solution Causal Impact. Since joining Integral in 2012, Kiril’s team of data scientists and engineers have worked on the industry’s most complex and challenging problems. Under his leadership, the team has developed solutions ranging from comprehensive media quality and risk management data, to best-in-breed fraud detection solution to the industry’s first campaign measurement solution based on causality. A theoretical physicist by training, Kiril holds a Ph.D. from the University of Washington. This talk was given on September 18, 2014 at Integral Ad Science’s industry symposium, The New Age of Advertising: Measuring Cause & Effect Online, in New York City. Explore Causal Impact further here: http://integralads.com/solutions/causal-impact/ Videography & Editing by Dan Hirshon http://www.DanHirshon.com
Views: 2011 Integral Ad Science
Gender-Related Research Methodology Feminist Research
Views: 130 Online Lectures In Hindi - Urdu
In the fast-paced world of tracked transactional execution, organizations worldwide have access to enormous volumes of data.“Big Data” is now the hot topic for organizational and managerial analytics. With access to this data, organizations can predict, target and leverage efforts to a much higher level than ever before. This short webinar will provide participants with knowledge of: • The definition of “Big Data” • An overview of “Big Data” mining techniques and challenges • Data visualization through a balanced scorecard and infographic overviews Presenter: Steven Rudnick, National and Canadian Legal Bill Review Manager, Zurich Financial and LFGSM Business Leader Faculty. Originally presented October 22, 2015
Views: 106 LakeForestMBA
What is FORENSIC PROFILING? What does FORENSIC PROFILING mean? FORENSIC PROFILING meaning - FORENSIC PROFILING definition - FORENSIC PROFILING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Forensic profiling is the study of trace evidence in order to develop information which can be used by police authorities . This information can be used to identify suspects and convict them in a court of law. The term "forensic" in this context refers to "information that is used in court as evidence" (Geradts & Sommer 2006, p. 10). The traces originate from criminal or litigious activities themselves. However traces are information that is not strictly dedicated to the court. They may increase knowledge in broader domains linked to security that deal with investigation, intelligence, surveillance, or risk analysis (Geradts & Sommer 2008, p. 26). Forensic profiling is different than offender profiling, which only refers to the identification of an offender to the psychological profile of a criminal. In particular, forensic profiling should refer to profiling in the information sciences sense, i.e., to "The process of 'discovering' correlations between data in data bases that can be used to identify and represent a human or nonhuman subject (individual or group), and/or the application of profiles (sets of correlated data) to individuate and represent a subject or to identify a subject as a member of a group or category" (Geradts & Sommer 2006, p. 41). Forensic profiling is generally conducted using datamining technology, as a means by which relevant patterns are discovered, and profiles are generated from large quantities of data. A distinction of forms of profiles that are used in a given context is necessary before evaluating applications of data mining techniques for forensic profiling. The data available to law enforcement agencies are divided into two categories (Geradts & Sommer 2008, p. 15): Nominal data directly designates persons or objects (recidivists, intelligence files and suspect files, stolen vehicles or objects, etc.) and their relations. Nominal data may also be obtained in the framework of specific investigations, for instance a list of calls made with a mobile phone (card and/or phone) that cover a certain period of time, a list of people corresponding to a certain profile, or data obtained through surveillances; Crime data consist of traces that result from criminal activities: physical traces, other information collected at the scene, from witness or victims or some electronic traces, as well as reconstructed descriptions of cases (modus operandi, time intervals, duration and place) and their relations (links between cases, series). The use of profiling techniques represents threats to the privacy of the individual and to the protection of fundamental freedoms. Indeed, criminal data, i.e., data which are collected and processed for suppressing criminal offences, often consists of personal data. One of the issues is the re-use of personal data collected within one criminal investigation for another purpose than the one for which it was collected. Several methods-including technical, legal, and behavioral-are available to address some of the issues associated with forensic profiling. For instance, in Europe the European Convention on Human Rights provides a number of instruments for the Protection of Individuals with regard to Automatic Processing of Personal Data.
Views: 472 The Audiopedia