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Accuracy, Recall and Precision
 
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www.stats-lab.com kobriendublin.wordpress.com Accuracy, Recall and Precision
Views: 46304 Dragonfly Statistics
Machine Learning: Testing and Error Metrics
 
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A friendly journey into the process of evaluating and improving machine learning models. - Training, Testing - Evaluation Metrics: Accuracy, Precision, Recall, F1 Score - Types of Errors: Overfitting and Underfitting - Cross Validation and K-fold Cross Validation - Model Evaluation Graphs - Grid Search
Views: 43509 Luis Serrano
Facebook scandal: Mark Zuckerberg admits errors over Cambridge Analytica data mining scandal
 
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Subscribe to France 24 now: http://f24.my/youtubeEN FRANCE 24 live news stream: all the latest news 24/7 http://f24.my/YTliveEN Facebook chief Mark Zuckerberg vowed Wednesday to "step up" to fix problems at the social media giant, as it fights a snowballing scandal over the hijacking of personal data from millions of its users. Visit our website: http://www.france24.com Subscribe to our YouTube channel: http://f24.my/youtubeEN Like us on Facebook: https://www.facebook.com/FRANCE24.English Follow us on Twitter: https://twitter.com/France24_en
Views: 892 FRANCE 24 English
Back Propagation in Neural Network with an example
 
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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: 163819 Naveen Kumar
Lecture 04 - Error and Noise
 
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Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy. Lecture 4 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 12, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 168438 caltech
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 71133 WekaMOOC
Forecasting: Moving Averages, MAD, MSE, MAPE
 
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This video shows how to calculate Moving Averages, and forecast error measures: The Mean Absolute Deviation or Error (MAD or MAE) The Mean Squared Error (MSE) and the Mean Absolute Percent Error (MAPE)
Views: 281264 Joshua Emmanuel
Classification accuracy assessment and errors
 
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Subject: Geology Paper: Remote sensing and GIS and GPS Module: Classification accuracy assessment and errors Content Writer: Dr. Manika Gupta
Views: 4154 Vidya-mitra
Decision Tree 5: overfitting and pruning
 
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Full lecture: http://bit.ly/D-Tree A decision tree can always classify the training data perfectly (unless there are duplicate examples with different class labels). In the process of doing this, the tree might over-fit to the peculiarities of the training data, and will not do well on the future data (test set). We avoid overfitting by pruning the decision tree.
Views: 113137 Victor Lavrenko
Integrating Human Behavior Modeling and Data Mining  to Predict Human Errors- Numerical Typing
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 59 Clickmyproject
Model Fitness - Mean Square Error(Test & Train error)
 
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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: 17914 Analytics University
Overfitting 2: training vs. future error
 
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[http://bit.ly/overfit] Training error is something we can always compute for a (supervised) learning algorithm. But what we want is the error on the future (unseen) data. We define the generalization error as the expected error of all possible data that could come in the future. We cannot compute it, but can approximate it with error computed over a testing set.
Views: 5056 Victor Lavrenko
Microsoft Data Mining Demo -- Highlight Exceptions
 
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Microsoft Data Mining Demo -- Highlight Exceptions with SQL Server 2008 and Excel 2007
Views: 2030 MarkTabNet
R operations   Data Cleaning,Error Correction and Data Transformation on airquality dataset
 
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THIS VIDEO SHOWS R OPERATIONS LIKE DATA CLEANING,ERROR CORECTION AND DATA TRANSFORMATION ON AIR QUALITY DATASET
Views: 12583 yogesh murumkar
Accuracy and Precision
 
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To see all my Chemistry videos, check out http://socratic.org/chemistry This is an easy to understand introduction to accuracy and precision. We'll play "guess my age," and look at bulls eyes representation of accuracy and precision.
Views: 224827 Tyler DeWitt
Linear Regression - Machine Learning Fun and Easy
 
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Linear Regression - Machine Learning Fun and Easy ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHIN LEARNING COURSE - http://augmentedstartups.info/machine-learning-courses ---------------------------------------------------------------------------- Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Dependent Variable – Variable who’s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents. And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- • To see how increase in sin tax has an effect on how many cigarettes packs are consumed • Sleep hours vs test scores • Experience vs Salary • Pokemon vs Urban Density • House floor area vs House price Forecast new observations – Can use what we know to forecast unobserved values Here are some other examples of ways that linear regression can be applied. • So say the sales of ROI of Fidget spinners over time. • Stock price over time • Predict price of Bitcoin over time. Linear Regression is also known as the line of best fit The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x You most likely learnt this in school. So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis. M is your slope or gradient, if you change this, then your line rotates along the intercept. Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series. So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 152514 Augmented Startups
【TOSHIBA】「Data mining」Productivity improvement at the manufacturing site
 
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Using Artificial Intelligence--or AI--to analyze “Big Data”and automatically identify the causes of manufacturing failures. Productivity improves dramatically.
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 77239 WekaMOOC
Mod-01 Lec-02 Data Mining, Data assimilation and prediction
 
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Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit http://nptel.ac.in
Views: 1938 nptelhrd
Accuracy and Precision
 
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This chemistry video tutorial explains the difference of accuracy and precision in measurement. This video gives an example of four students attempting to measure the density of aluminum and asks which data is accurate but not precise. Accuracy has to do with how close your data is to the accepted value and precision has to do with how close your data is with each other. New Chemistry Video Playlist: https://www.youtube.com/watch?v=bka20Q9TN6M&t=25s&list=PL0o_zxa4K1BWziAvOKdqsMFSB_MyyLAqS&index=1 Access to Premium Videos: https://www.patreon.com/MathScienceTutor
K-Mean Clustering
 
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Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 139211 Anuradha Bhatia
Evaluation of Data Mining Models for Numerical Predictions
 
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This video shows how to calculate MSEs (Mean Squared Errors) to evaluate data mining models for numerical predictions.
Views: 20 Ben KIM
Startup uses machine learning to identify programming errors with log analytics
 
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Coralogix applies machine learning to the analysis of software logs to find errors quickly. More than 70 percent of resolution time is wasted on discovering errors and bugs in corporate software, while only 30 percent is spent fixing them. The cost of this labor burden is a serious incentive for companies to seek out more advanced solutions. At Coralogix, we recognize that highly paid software engineers shouldn’t have to waste valuable time deciphering boatloads of unstructured string data (the standard format of software logs) when they could focus on creating products instead. They need a tool that will do the slogging for them—and do it right.
Precision, Recall & F-Measure
 
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In this video, we discuss performance measures for Classification problems in Machine Learning: Simple Accuracy Measure, Precision, Recall, and the F (beta)-Measure. We explain the concepts in detail, highlighting differences between the terms, introducing Confusion Matrices, and analyzing real world examples. If you like the video, please SHARE. Don't forget to like, comment and SUBSCRIBE on your way out! If you have any questions, feel free to contact me. Email: [email protected]
Views: 15735 CodeEmporium
Regression I: What is regression? | SSE, SSR, SST | R-squared | Errors (ε vs. e)
 
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All videos here: http://www.zstatistics.com/ The first video in a series of 5 explaining the fundamentals of regression. Please note that in my videos I use the abbreviations: SSR = Sum of Squares due to the Regression SSE = Sum of Squares due to Error. Intro: 0:00 Y-hat line: 2:26 Sample error term, e: 3:47 SSR, SSE, SST: 8:40 R-squared intro: 9:43 Population error term, ε: 12:11 Second video here: http://www.youtube.com/watch?v=4otEcA3gjLk Ever wondered WHY you have to SQUARE the error terms?? Here we deal with the very basics: what is regression? How do we establish a relationship between two variables? Why must we SQUARE the error terms? What exactly is SSE, SSR and SST? What is the difference between a POPULATION regression function and a SAMPLE regression line? Why are there so many different types of error terms?? Enjoy.
Views: 657961 zedstatistics
Confidence intervals and margin of error | AP Statistics | Khan Academy
 
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Confidence intervals and margin of error. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/estimating-confidence-ap/introduction-confidence-intervals/v/confidence-intervals-and-margin-of-error?utm_source=youtube&utm_medium=desc&utm_campaign=apstatistics AP Statistics on Khan Academy: Meet one of our writers for AP¨_ Statistics, Jeff. A former high school teacher for 10 years in Kalamazoo, Michigan, Jeff taught Algebra 1, Geometry, Algebra 2, Introductory Statistics, and AP¨_ Statistics. Today he's hard at work creating new exercises and articles for AP¨_ Statistics. Khan Academy is a nonprofit organization with the mission of providing a free, world-class education for anyone, anywhere. We offer quizzes, questions, instructional videos, and articles on a range of academic subjects, including math, biology, chemistry, physics, history, economics, finance, grammar, preschool learning, and more. We provide teachers with tools and data so they can help their students develop the skills, habits, and mindsets for success in school and beyond. Khan Academy has been translated into dozens of languages, and 15 million people around the globe learn on Khan Academy every month. As a 501(c)(3) nonprofit organization, we would love your help! Donate or volunteer today! Donate here: https://www.khanacademy.org/donate?utm_source=youtube&utm_medium=desc Volunteer here: https://www.khanacademy.org/contribute?utm_source=youtube&utm_medium=desc
Views: 246673 Khan Academy
Gini index based Decision Tree
 
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How does a Decision Tree Work? A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Splitting stops when every subset is pure (all elements belong to a single class) Code for visualising a decision tree - https://github.com/bhattbhavesh91/visualize_decision_tree If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: GitHub - https://github.com/bhattbhavesh91 Medium - https://medium.com/@bhattbhavesh91 #decisiontree #Gini #machinelearning #python #giniindex
Views: 32336 Bhavesh Bhatt
Linear Regression - Least Squares Criterion  Part 1
 
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Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Linear Regression - Least Squares Criterion. In this video I just give a quick overview of linear regression and what the 'least square criterion' actually means. In the second video, I will actually use my data points to find the linear regression / model.
Views: 458724 patrickJMT
How to Clean Up Raw Data in Excel
 
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Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects. Subscribe to Skillshare’s Youtube Channel: http://skl.sh/yt-subscribe Check out all of Skillshare’s classes: http://skl.sh/youtube Like Skillshare on Facebook: https://www.facebook.com/skillshare Follow Skillshare on Twitter: https://twitter.com/skillshare Follow Skillshare on Instagram: http://instagram.com/Skillshare
Views: 100876 Skillshare
Social Media Data Mining with Raspberry Pi (Part 6: Debugging)
 
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So you've written that conceptually brilliant program... but it just won't run. Time to debug -- to find and fix the errors. But how? There are many complicated technical methods for professional debugging, but for the program-writing neophyte, seven basic tips can really help with most debugging challenges. This video is part 6 in a YouTube series for absolute beginners among non-STEM students on taking the Raspberry Pi and using it as a cheap and robust platform for social media data mining and analysis.
Views: 919 James Cook
R tutorial: Cross-validation
 
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Learn more about machine learning with R: https://www.datacamp.com/courses/machine-learning-toolbox In the last video, we manually split our data into a single test set, and evaluated out-of-sample error once. However, this process is a little fragile: the presence or absence of a single outlier can vastly change our out-of-sample RMSE. A better approach than a simple train/test split is using multiple test sets and averaging out-of-sample error, which gives us a more precise estimate of true out-of-sample error. One of the most common approaches for multiple test sets is known as "cross-validation", in which we split our data into ten "folds" or train/test splits. We create these folds in such a way that each point in our dataset occurs in exactly one test set. This gives us 10 test sets, and better yet, means that every single point in our dataset occurs exactly once. In other words, we get a test set that is the same size as our training set, but is composed of out-of-sample predictions! We assign each row to its single test set randomly, to avoid any kind of systemic biases in our data. This is one of the best ways to estimate out-of-sample error for predictive models. One important note: after doing cross-validation, you throw all resampled models away and start over! Cross-validation is only used to estimate the out-of-sample error for your model. Once you know this, you re-fit your model on the full training dataset, so as to fully exploit the information in that dataset. This, by definition, makes cross-validation very expensive: it inherently takes 11 times as long as fitting a single model (10 cross-validation models plus the final model). The train function in caret does a different kind of re-sampling known as bootsrap validation, but is also capable of doing cross-validation, and the two methods in practice yield similar results. Lets fit a cross-validated model to the mtcars dataset. First, we set the random seed, since cross-validation randomly assigns rows to each fold and we want to be able to reproduce our model exactly. The train function has a formula interface, which is identical to the formula interface for the lm function in base R. However, it supports fitting hundreds of different models, which are easily specified with the "method" argument. In this case, we fit a linear regression model, but we could just as easily specify method = 'rf' and fit a random forest model, without changing any of our code. This is the second most useful feature of the caret package, behind cross-validation of models: it provides a common interface to hundreds of different predictive models. The trControl argument controls the parameters caret uses for cross-validation. In this course, we will mostly use 10-fold cross-validation, but this flexible function supports many other cross-validation schemes. Additionally, we provide the verboseIter = TRUE argument, which gives us a progress log as the model is being fit and lets us know if we have time to get coffee while the models run. Let's practice cross-validating some models.
Views: 48318 DataCamp
Normal Distribution - Explained Simply (part 1)
 
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*** IMPROVED VERSION of this video here: https://youtu.be/tDLcBrLzBos I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1135286 how2stats
Lecture 21
 
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Brief introduction on Key terminologies: Prediction performance Prediction Error Predictive Accuracy Measures
R Tutorial 16: Special conditionals and common mistakes with If-Else statements
 
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How to check conditions and write IF-ELSE statements in R, A common mistake people commit while writing ELSE statments is discussed. The software that is used for data mining / machine learning / data science / statistical computing / business analytics and mathematical problem solving. For more detailed discussions on various topics checkout: http://rstatistics.net/ http://rstatistics.net/r-tutorial-exercise-for-beginners/ Get regular awesome tips on R programming twitter: http://twitter.com/r_programming Like our 'One R Tip A Day' facebook page and check get notifications in the 'like' button dropdown to get nice R tips on your news feed every day! http://facebook.com/rtipaday The software that is used for data mining / machine learning / data science / statistical computing / business analytics and mathematical problem solving. For more detailed discussions on various topics checkout: http://rstatistics.net/ http://rstatistics.net/r-tutorial-exercise-for-beginners/ Get regular awesome tips on R programming twitter: http://twitter.com/r_programming Like our 'One R Tip A Day' facebook page and check get notifications in the 'like' button dropdown to get nice R tips on your news feed every day! http://facebook.com/rtipaday Subscribe NOW! by clicking the 'Subscribe Button'. For Best Results, watch in HD. R is world's most widely used open source statistical programming language. It's the # 1 choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in businesses worldwide. A Channel dedicated to R Programming - The language of Data Science. We notice people learning the language in parts, so the initial lectures are dedicated to teach the language to aspiring Data Science Professionals, in a structured fashion so that you learn the language completely and be able to appreciate the inherent brilliance of the language and be able to contribute back to the community. If you are wondering where R stands currently in terms of desirabilty of the skill, check the the following reports in the links below: http://marketing.dice.com/pdf/Dice_TechSalarySurvey_2014.pdf To get a brief idea of what areas the language can be applied, check out the taskviews in the following page: http://cran.r-project.org/web/views/ This latest R Programming Course for Data Science is most suitable for Non-Programmer statisticians and Newbies who want to become the most coveted Data science professional that most companies are looking for.
Views: 23395 LearnR
What is backpropagation really doing? | Deep learning, chapter 3
 
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What's actually happening to a neural network as it learns? Next video: https://youtu.be/tIeHLnjs5U8 Brought to you by you: http://3b1b.co/nn3-thanks And by CrowdFlower: http://3b1b.co/crowdflower Home page: https://www.3blue1brown.com/ The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these "nudges" in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen's book or Chis Olah's blog.
Views: 1149137 3Blue1Brown
Data Mining with Weka (4.2: Linear regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Linear regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 43987 WekaMOOC
Zuckerberg admits to mistakes over Facebook privacy scandal
 
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Facebook is facing its worst privacy scandal following allegations that Cambridge Analytica, a Trump-affiliated data mining firm, used ill-gotten data from millions of users to influence elections. According to Facebook, as many as 87 million people might have had their data accessed. Congressional officials said Zuckerberg would testify next week to talk about Facebook’s use and protection of user data. Subscribe to us on YouTube: https://goo.gl/lP12gA Download our APP on Apple Store (iOS): https://itunes.apple.com/us/app/cctvnews-app/id922456579?l=zh&ls=1&mt=8 Download our APP on Google Play (Android): https://play.google.com/store/apps/details?id=com.imib.cctv Follow us on: Facebook: https://www.facebook.com/ChinaGlobalTVNetwork/ Instagram: https://www.instagram.com/cgtn/?hl=zh-cn Twitter: https://twitter.com/CGTNOfficial Pinterest: https://www.pinterest.com/CGTNOfficial/ Tumblr: http://cctvnews.tumblr.com/ Weibo: http://weibo.com/cctvnewsbeijing
Views: 331 CGTN
Data Science - 8.1.4 - In and out of sample errors
 
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Data Science PLAYLIST: https://tinyurl.com/DataSciencePlaylist Unit 8: Practical Machine Learning Part 1: Predictions, Errors and Cross-Validation Lesson: 4 - In and out of sample errors Notes: https://tinyurl.com/DataScienceNotes
Views: 10 Bob Trenwith
Forecasting: Exponential Smoothing, MSE
 
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This video shows how to calculate exponential smoothing and the Mean Squared Error.
Views: 213129 Joshua Emmanuel
Tasks with multiple instances, mapping relevant data and communicating errors
 
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Explanation on how to implement tasks with multiple instances and the various ways in which we can determine the number of instances that will be executed.
Views: 38 GeneXus™
Avoid typing and data entry errors using Data Validation
 
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This video is the first of the Data Validation series which will teach you how to avoid typing or data entry errors in Excel. This technique will also make it very easy to handle and maintain spreadsheets. Link to practice file - https://drive.google.com/open?id=18nVRMCctvRm6YNSQSCIAOpZ-CRlZd4oN
Views: 57 Awesh Bhornya
How to Fix Memory Management Error In Windows 7/8/10
 
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This tutorial focuses on the BSOD (Blue Screen Of Death) error that comes from a Memory Management Error "0X0000001A". If you’re on Windows 10 and you’re seeing the blue screen of death error MEMORY_MANAGEMENT, you’re not alone. Many Windows users are reporting this problem as well. But no worries, it’s possible to fix, even though it might be related to severe memory management error. This tutorial will apply for computers, laptops, desktops,and tablets running the Windows 10, Windows 8/8.1, Windows 7 operating systems.Works for all major computer manufactures (Dell, HP, Acer, Asus, Toshiba, Lenovo, Samsung).
Views: 343013 MDTechVideos
CODE 43 Error fixing method for windows 7 64 bit and 32bit
 
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Windows 7 code 43 error fixing trick How to fix errors on windows 7 64 bit and 32 bit Steps : 1: Registry 2: Fnd usbstore and change value 3: Go to system32 4: find file Repository folder 5: follow the video for better understanding This video i made only for educational purpose [For more Like and Subscribe ] windows error log Windows error reporting "windows codes" error windows error logs windows error codes lookup windows error log windows 7 software error codes USB Driver Solutions Windows help Error fixing Easy way to fix windows 7 errors USB error Pen drive errors CODE 43 USB drivers problems and solutions Pc helps Computer problem fixing tips computer errors list error handling computer errors codes Fix all type error codes vCopyright Reserved © - 2015-16 All rights to this video is owned DEEZ : deezzone.com The video obeys the YouTube Community Guidelines and NO copyright content is present in this video. For Educational Purpose Only. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for -fair use- for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favour of fair use Copyright Reserved © - 2015-16Copyright Reserved © - 2015-16 All rights to this video is owned DEEZ : deezzone.com The video obeys the YouTube Community Guidelines and NO copyright content is present in this video. For Educational Purpose Only. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for -fair use- for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favour of fair use Copyright Reserved © - 2015-16
Views: 593090 DEE Z
Using Key Word Analysis of an Organization’s Big Data for Error and Fraud Detection
 
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Using Key Word Analysis of an Organization’s Big Data for Error and Fraud Detection- December 3, 2014 Webinar Please see www.auditnet.org for further details on the textual analytics survey and associated webinar.
Views: 455 AuditNet LLC
Bootstrap Resampling
 
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This video provides an introduction to the technique of bootstrap resampling, which is a computational method of measuring the error in a statistic's estimator.
Views: 105542 Nick Hand