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What is machine learning and how to learn it ?
 
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http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com
Views: 552953 Hitesh Choudhary
Bitcoin: How Cryptocurrencies Work
 
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Whether or not it's worth investing in, the math behind Bitcoin is an elegant solution to some complex problems. Hosted by: Michael Aranda Special Thanks: Dalton Hubble Learn more about Cryptography: https://www.youtube.com/watch?v=-yFZGF8FHSg ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters—we couldn't make SciShow without them! Shout out to Bella Nash, Kevin Bealer, Mark Terrio-Cameron, Patrick Merrithew, Charles Southerland, Fatima Iqbal, Benny, Kyle Anderson, Tim Curwick, Will and Sonja Marple, Philippe von Bergen, Bryce Daifuku, Chris Peters, Patrick D. Ashmore, Charles George, Bader AlGhamdi ---------- Like SciShow? Want to help support us, and also get things to put on your walls, cover your torso and hold your liquids? Check out our awesome products over at DFTBA Records: http://dftba.com/scishow ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://bitinfocharts.com/ https://chrispacia.wordpress.com/2013/09/02/bitcoin-mining-explained-like-youre-five-part-2-mechanics/ https://www.youtube.com/watch?v=Lx9zgZCMqXE https://www.youtube.com/watch?v=nQZUi24TrdI https://bitcoin.org/en/how-it-works http://www.forbes.com/sites/investopedia/2013/08/01/how-bitcoin-works/#36bd8b2d25ee http://www.makeuseof.com/tag/how-does-bitcoin-work/ https://blockchain.info/charts/total-bitcoins https://en.bitcoin.it/wiki/Controlled_supply https://www.bitcoinmining.com/ http://bitamplify.com/mobile/?a=news Image Sources: https://commons.wikimedia.org/wiki/File:Cryptocurrency_Mining_Farm.jpg
Views: 2444017 SciShow
What REALLY is Data Science? Told by a Data Scientist
 
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Views: 127702 Joma Tech
BADM 1.1: Data Mining Applications
 
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This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 1922 Galit Shmueli
Facebook CEO Mark Zuckerberg testifies before Congress on data scandal
 
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Facebook CEO Mark Zuckerberg will testify today before a U.S. congressional hearing about the use of Facebook data to target voters in the 2016 election. Zuckerberg is expected to offer a public apology after revelations that Cambridge Analytica, a data-mining firm affiliated with Donald Trump's presidential campaign, gathered personal information about 87 million users to try to influence elections. »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 124227 CBC News
Percentiles and Quartiles
 
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statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
Views: 327121 statslectures
Zaloni Zip: Data Quality
 
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In this Zaloni Zip, Adam Diaz discusses the process of separating good data from bad data. To explore more topics related to your data, please visit: https://resources.zaloni.com/blog/zaloni-zip-data-quality-4
Views: 476 Zaloni
Lecture 01 - The Learning Problem
 
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The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 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 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 771743 caltech
Cloud OnAir: Deploy like Netflix and Google with Automated Canary Analysis
 
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Continuous delivery at any scale necessitates reliably delivering software releases at high velocity. Teams often use manual or ad-hoc canary analysis to make their delivery process safe and reliable. However, with speed and scalability challenges along with risk of human bias, such canary analysis can result in bad deployments being pushed to production. Join Google and Netflix team to more learn about Kayenta, an open automated canary analysis service. Learn how Kayenta can help you accurately assess the risk of canary release using nonparametric statistical tests and let you build a fast, safe, repeatable deployment pipeline.
Views: 1481 Google Cloud Platform
Machine Learning for Survival Analysis: Theory, Algorithms and Applications part 1
 
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Authors: Yan Li, University of Michigan Chandan K. Reddy, Department of Computer Science, Virginia Polytechnic Institute and State University Abstract: Due to the advancements in various data acquisition and storage technologies, different disciplines have attained the ability to not only accumulate a wide variety of data but also to monitor observations over longer time periods. In many real-world applications, the primary objective of monitoring these observations is to estimate when a particular event of interest will occur in the future. One of the major difficulties in handling such problem is the presence of censoring, i.e., the event of interests is unobservable in some instance which is either because of time limitation or losing track. Due to censoring, standard statistical and machine learning based predictive models cannot readily be applied to analyze the data. An important subfield of statistics called survival analysis provides different mechanisms to handle such censored data problems. In addition to the presence of censoring, such time-to-event data also encounters several other research challenges such as instance/feature correlations, high-dimensionality, temporal dependencies, and difficulty in acquiring sufficient event data in a reasonable amount of time. To tackle such practical concerns, the data mining and machine learning communities have started to develop more sophisticated and effective algorithms that either complement or compete with the traditional statistical methods in survival analysis. In spite of the importance of this problem and relevance to real-world applications, this research topic is scattered across various disciplines. In this tutorial, we will provide a comprehensive and structured overview of both statistical and machine learning based survival analysis methods along with different applications. We will also discuss the commonly used evaluation metrics and other related topics. The material will be coherently organized and presented to help the audience get a clear picture of both the fundamentals and the state-of-the-art techniques. Link to tutorial: http://dmkd.cs.vt.edu/TUTORIAL/Survival/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 442 KDD2017 video
Why learn EDA? - Data Analysis with R
 
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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. 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: 9500 Udacity
The Fundamentals of Predictive Analytics - Data Science Wednesday
 
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Data Science Wednesday is produced by Decisive Data, a data analytics consultancy. Lean more about us using the following links. Also, the video transcription is included below. http://www.decisivedata.net https://twitter.com/DecisiveData https://www.linkedin.com/company/decisive-data/ Video Transcription: What is Predictive Analytics Hello, and welcome back to Data Science Wednesday. My name is Tessa Jones, and I'm a data scientist with Decisive Data. And today we're gonna talk about predictive analytics and what it can do for you. Predictive analytics fits into the spectrum of analytics that we've talked about before. Starting with descriptive, which is the most basic of the analytics, it's basically just cleaning, relating, summarizing, and visualizing your data, really getting to the questions about what's happening in my business. And then there's diagnostic, which is really getting down to why things are happening. What's causing my revenue to decline or to increase? How are things related? Things like that. So if you've got a good base in both of these, then we're ready to move into predictive analytics, which is gonna dive into what's gonna happen in the future, which is super powerful. If you're a business person and you want to be able to make good business questions, if you have at least an idea of what might happen in the future, your answers are already gonna be a little bit better. So, let's dive in. So, let's go with an example because that just makes it easier to kind of flow through what's actually happening here. So let's pretend that we are grocery store owners. And if we're already talking about predictive analytics, you should have a pretty good grasp on descriptive and predictive and diagnostic analytics. So, you probably already have a decent dashboard that really tells you what's happening in your business right now. So, something like this where you have, you know, something here that tells you revenue by different departments like foods and pastry, or how your sales changes by product over time, things like that. So you have an idea of what's happening in your business, but now you really wanna know, what's gonna happen in my business? So one really common question is, how many of a given product am I gonna sell for every store? Because this can really answer questions around how you're gonna support supply chain processes, or how you're gonna manage the profits that you're going to have. Things like that. So the first thing we need to do is talk about what happened in the past. We really can't do anything or predict very easily unless we know or at least have an idea of what's happened in the past. So here we have three lines in black that represent, basically, historical data. Each line here is one year worth of sales for a given product. And then the green line here is the current year. And here's today. And if we build a predictive model, it's gonna tell us what's gonna happen for the rest of the year. So if this is all set up and we build a model, basically, we mix this information with all the data that's really clean and well-organized, we mash it together with a bunch of mathematics and coding, and basically, we pop out some results and it shows up in a visual like this where you have, these are the sales that we have had and these are the sales that we think we're going to have. So a business person can look at this chart and say, "Wow, we need to put a lot more products to this store because I see sales are gonna increase." Or, "Our profit margins are gonna be way higher than we thought so we can start a new program." Things like that. You can really start to get innovative with your business decisions. So, let's pretend we've built this model and it's been running for a year. And now we wanna know how well is this model actually performing? So down here, we have a chart that shows, in black, what we actually sold, and then in green, what we thought we were going to sell. And we see that there's a couple of pretty big misses. Right here, we sold way more than we thought we would, which leaves risk to, you know, missing out on inventory. Or, here, we predicted we would sell way more than we did. So both of these are kind of misses. And so we need to go back and look at the data and understand what assumptions we applied that were maybe a little bit wrong, or applied incorrectly, or look at the data, maybe we weren't accounting for something and we kind of reorganize that and incorporate it into the model. And then we redeploy it, and then we have a better model.
Views: 1169 Decisive Data
Ensemble Learning Boosting - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-367378584/m-367548595 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: 28482 Udacity
Creationism, God & Evolution - Poll
 
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http://www.huffingtonpost.com/2010/12/20/40-of-americans-still-bel_n_799078.html New TYT Network channels: http://www.youtube.com/user/tytsports http://www.youtube.com/user/thetopvlog New TYT Facebook Page(!): Follow us on Twitter: http://twitter.com/theyoungturks http://www.theyoungturks.com/membership DISCOUNTS: http://www.theyoungturks.com/godaddy FREE Movies(!): http://www.netflix.com/tyt Note: The above two links are for TYT sponsors. Read Ana's blog and subscribe at: http://www.examiner.com/x-5445-Politics-in-Education-Examiner TYT Network (new WTF?! channel): http://www.youtube.com/user/whattheflickshow Check Out TYT Interviews http://www.youtube.com A new Gallup poll, released Dec. 17, reveals that 40 percent of Americans still believe that humans were created by God within the last 10,000 years. This number is slightly down from a previous high of 47 percent in 1993 and 1999. Another 38 percent of respondents believe that humans have evolved from more basic organisms but with God playing a role in the process. A mere 16 percent of respondents subscribed to the belief of "secular evolution": that humans have evolved with no divine guidance. However, this number has nearly doubled from nine percent of respondents in a poll from 1982. The poll also revealed that beliefs in creationism and evolution are strongly related to levels of education attained. When results are narrowed to those with college degrees, only 37 percent of respondents maintain beliefs in creationism. Meanwhile, the belief in evolution without the aid of God rises to 21 percent. With regards to political affiliation, a majority of Republicans (52 percent) subscribe to creationist beliefs. This is compared to only 34 percent among Democrats and Independents. Views on human origins vary based on church attendance. Of those who attend church on a weekly basis, 60 percent believe in creationism while a mere 2 percent subscribe to "secular evolution". These numbers are flipped among those who rarely or never attend religious services. In this group, only 24 percent believe in creationism while 39 percent believe in evolution without divine guidance. This represents the only subset of data reported where "secular evolution" beats out creationism.
Views: 40953 The Young Turks
Random Forest Using R: Step by Step Tutorial
 
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You can download the "Credit Card Dataset" from the below link: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients Learn Data Science & Machine Learning by doing! Hands On Experience Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This course is for those : 1. Who wants to be Data Scientist 2. Who are working as analyst / software developer but wants to be Data Scientist What is Data Science ? Data science is used to extract patterns or insights from data to predict future or to understand customer behavior and so on. Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data Mining large amounts of structured and unstructured data to identify patterns can help an organization to reduce costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage. Some Data Science and machine learning Applications Netflix uses data science & machine learning to mine movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce. Companies like Flipkart and Amazon uses data science and machine learning to understand the customer shopping behavior to do better recommendations. Gmail's spam filter uses data science (machine learning algorithm) to process incoming mail and determines if a message is junk or not.. Proctor & Gamble utilizes data science (machine learning ) models to more clearly understand future demand, which help plan for production levels more optimally. Why Programming Won't Work in some Cases?? Have you ever thought of the scenario where all the cars will be moving without a driver that means something like automated machines say for example automatic washing machine. But there is a difference. 1. For automatic washing machine,we can write programs for the washing machine functionality. 2. For automated cars without drivers in high traffic.Just imagine ,how complex and dangerous it will be when someone starts coding /programming for such functionalities.For cars to automate we would require something which is called "Machine Learning " In this course, we are first going to first discuss Data Structures,etc. in R like : 1. Vectors 2. Matrices 3. Data Frames 4. Factors 5. Numerical/Categorical Variables 6. List 7. How to convert matrix into data frame Programming in R Data Visualization Then implementation/working of machine learning models like 1. Linear Regression 2. Decision Tree 3. Random Forest 4.Neural Networks 5. Deep learning 6. H2o framework 7. Cross validation /How to avoid Over fitting 8. Dimensionality Reduction Techniques All the materials for this data science & machine learning course are FREE. You can download and install R, with simple commands on Windows, Linux, or Mac. This course focuses on "how to build and understand", not just "how to use".It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally.
How Industrial IoT is Influenced by Cognitive Anomaly Detection
 
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The Industrial IoT sector is facing maintenance challenges related to their data processes. Traditional methods of anomaly detection aren’t providing the right solutions for every entity, which is why Cognitive Anomaly detection is filling the gaps in predictive maintenance. Watch full webinar here https://www.youtube.com/watch?v=xVi-JyJjGAs My name is Ronald Van Loon and i would like to invite you to join me on journey through the intelligent world, and have a deeper look into technological development that are shaping a world and transforming business. Subscribe To My Channel https://goo.gl/hhsA85 As a recognized expert and thought leader in this field, I work with data-driven companies to generate business value so that they may meet and exceed goal after goal. I have been recognized for my work in the field of digital transformation by such publications and organizations as Onalytica, Dataconomy, and Klout. In addition to these recognitions, I am also an author for a number of leading big data websites, including The Guardian, The Datafloq, and Data Science Central, and I regularly speak at renowned events and conferences. How I Help Companies Generate Business Value I write relevant articles & host webinars on Artificial Intelligence, Machine Learning, big data, IoT, data science, analytics and other digital transformation topics, which I distribute to an audience of over 100,000 social media fans and followers. ➨FOLLOW & Read my articles: http://bit.ly/297s0zU ➨Join my webinars:http://bit.ly/2iMM5Qd ➨ To connect with me for article writing, hosting webinars or attend events mail me at [email protected] By learning how to make the best use of the data your company has, and by becoming certified, you can boost your career! ➨Learn more how I can help you as a Simplilearn course advisor http://bit.ly/2hHu8lo As the director of Adversitement, I work to help data-driven businesses become more successful. ➨To learn more contact me via [email protected] Stay Up to Date on the Latest News and Insights in Data Transformation ➨Join my LinkedIn group, “Awesome Ways Big Data Is Used to Improve Our World”, at http://ow.ly/GXoYi. ➨Connect with me on LinkedIn, and follow my publications via LinkedIn Pulse at http://bit.ly/1LtwD3Z. ➨Follow me on Twitter @Ronald_vanLoon
Views: 3787 Ronald Van Loon
The Third Industrial Revolution: A Radical New Sharing Economy
 
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The global economy is in crisis. The exponential exhaustion of natural resources, declining productivity, slow growth, rising unemployment, and steep inequality, forces us to rethink our economic models. Where do we go from here? In this feature-length documentary, social and economic theorist Jeremy Rifkin lays out a road map to usher in a new economic system. A Third Industrial Revolution is unfolding with the convergence of three pivotal technologies: an ultra-fast 5G communication internet, a renewable energy internet, and a driverless mobility internet, all connected to the Internet of Things embedded across society and the environment. This 21st century smart digital infrastructure is giving rise to a radical new sharing economy that is transforming the way we manage, power and move economic life. But with climate change now ravaging the planet, it needs to happen fast. Change of this magnitude requires political will and a profound ideological shift. To learn more visit: https://impact.vice.com/thethirdindustrialrevolution Click here to subscribe to VICE: http://bit.ly/Subscribe-to-VICE Check out our full video catalog: http://bit.ly/VICE-Videos Videos, daily editorial and more: http://vice.com More videos from the VICE network: https://www.fb.com/vicevideo Click here to get the best of VICE daily: http://bit.ly/1SquZ6v Like VICE on Facebook: http://fb.com/vice Follow VICE on Twitter: http://twitter.com/vice Follow us on Instagram: http://instagram.com/vice Download VICE on iOS: http://apple.co/28Vgmqz Download VICE on Android: http://bit.ly/28S8Et0
Views: 2567541 VICE
Data Science Methodology 101 - Business Understanding Concepts and Case Study
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/data-science-methodology-2/ Data Science Methodology Grab you lab coat, beakers, and pocket calculator…wait what? wrong path! Fast forward and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed. Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. Learn the major steps involved in tackling a data science problem. Learn the major steps involved in practicing data science, with interesting real-world examples at each step: from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. https://bigdatauniversity.com/courses/data-science-methodology-2/
Views: 12758 Cognitive Class
Lecture 12 - Regularization
 
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Regularization - Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay. Lecture 12 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 May 10, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 80551 caltech
Fix lsass.exe High CPU Usage on Windows
 
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This video is related to the Windows lsass.exe process which sometimes shows high CPU usage. lsass.exe is the 'Local Security Authority Process', also known as the 'Local Security Authentication Server'.
Views: 36169 codekisk
Database Lesson #8 of 8 - Big Data, Data Warehouses, and Business Intelligence Systems
 
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Dr. Soper gives a lecture on big data, data warehouses, and business intelligence systems. Topics covered include big data, the NoSQL movement, structured storage, the MapReduce process, the Apache Cassandra data model, data warehouse concepts, multidimensional databases, business intelligence (BI) concepts, and data mining,
Views: 73221 Dr. Daniel Soper
11. Introduction to Machine Learning
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 306690 MIT OpenCourseWare
Intro to Big Data, Data Science & Predictive Analytics
 
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We introduce you to the wide world of Big Data, throwing back the curtain on the diversity and ubiquity of data science in the modern world. We also give you a bird's eye view of the subfields of predictive analytics and the pieces of a big data pipeline. -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 2700+ employees from over 400 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2mD3ziB See what our past attendees are saying here: http://bit.ly/2nwIN2A -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 8270 Data Science Dojo
Kubernetes for Enterprise Security Requirements (Cloud Next '18)
 
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An increasing number of enterprises see containers as the next step in their infrastructure’s evolution, but are blocked by security, compliance, and other regulatory requirements. At the same time, large corporations are already running workloads in production. So how are they doing it? In this talk, we’ll go through some of the most common enterprise security requirements, discuss how you can use native Kubernetes features and other tools to meet these needs, and what specifically, a security-focused company like Fleetsmith is doing. Event schedule → http://g.co/next18 Watch more Security sessions here → http://bit.ly/2zJTZml Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Lecture 17 - Three Learning Principles
 
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Three Learning Principles - Major pitfalls for machine learning practitioners; Occam's razor, sampling bias, and data snooping. Lecture 17 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 May 29, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 62398 caltech
Micron Technology ($MU): Technical Analysis of Monthly, Weekly, and Daily data points
 
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Website: https://tritontrades.com Facebook: https://www.facebook.com/tritontrades/ Twitter: https://twitter.com/AlexanderFB89 Disclaimer: All information is shared for educational purposes only and are not solicitations or recommendations to buy or sell securities. Each person must conduct their own research, analysis, and risk-assessment before every trade. None of this information is to be construed as investment and trading advice. No one at Triton Trades is a registered investment adviser, broker dealer, or in any other way qualified to give financial advice. Any use you make of our content is at your own risk and your own responsibility. You hereby agree that you shall not make any financial, investment, legal and/or other decision based in whole or in part on anything contained in our Website or Services. There is no guarantee that the information on www.tritontrades.com (or related sites) is correct, complete, or current. Further, you accept that www.tritontrades.com could experience technical problems rendering parts or all of the website unavailable at any time. www.tritontrades.com is protected by iThemes Security and Cloudflare, but there is no guarantee that its free from viruses. There may be ads or sponsorship on this website, and you accept that Triton Trades is not in any way responsible for your use of such content. You accept that Triton Trades does not offer refunds for any of its products or services. You understand that Triton Trades is represented by Alexander Bjerkvik, and that Triton Trades is not a registered organization/business. Owners, employees, agents or representatives of Triton Trades may have interests or positions in securities of the entities profiled herein. Specifically, such parties may buy or sell positions, and may or may not follow the information provided on this Website. Some or all of the positions may have been acquired prior to the publication of such information on the Website, and such positions may increase or decrease at any time. All trading involve serious risks, and you can lose your entire investment. Additionally, you may lose more than your entire investment if you are trading futures or trading on margin.
Views: 57 TritonTrades
Reco4: A Recommendation Engine Exploiting Machine Learning on Big Data
 
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This session takes a close look at the Reco4 recommender, a Java graph-based recommendation engine. Today the most advanced types of these functionalities are available only to internet giants. This framework is targeted toward the implementation and improvement of state-of-the-art algorithms in this promising research field, as well as their diffusion among a broader audience, using an on-premises or cloud service solution. Reco4 leverages collaborative filtering techniques, content-based analysis, and data mining techniques. It supports composable algorithms, social recommendations, and multitenancy. The graph databases and Hadoop/YARN distributed processing system and task management are integrated to guarantee big data support. Authors: Alessandro Negro Alessandro Negro, CTO of Wirex and Founder and Project Leader of Reco4J, has been working with Data Management Technology since 2003 when he took part to the GRelC project, that aimed at providing a set of advanced data grid services to transparently, efficiently and securely manage databases on the Grid Environment. He started working on Big Data in 2008 when he worked for Euro Mediterranean Centre For Climate Change (CMCC), where he deployed a metadata handling system to handle the huge quantity of data produced by the centre and from other climate change related centres that took part to the ClimateG testbed. He has published widely on Grid Data Management and Metadata Management to conferences proceedings and journals. He holds a BSc in Computer Engineering, MSc in Computer Engineering, and a PhD in Interdisciplinary Science and Technology. He is a Member of IEEE, IEEE Computer Society and ACM since 2007. His interests range from the software architecture to Data Management, from programming languages to Linux and UNIX, from Cloud Computing to Machine Learning, from Data Warehouse and Data Mining to NoSQL database. View more trainings by Alessandro Negro at https://www.parleys.com/author/alessandro-negro Luigi Giuri Luigi Giuri is an entrepreneur, researcher, and professional in the computer science and information technology sector. He is currently Chairman and CEO at Wirex, a software development company based in Italy. Since 2000 he started taking an entrepreneurial approach to science and technology. After three years devoted to development of Internet portals and e-commerce solutions for customers like Telecom Italia, Wind, and Mastercard, in 2003 he co-founded Wirex to concentrate on the development of online gaming business solutions. Under his guide the company started with a state of the art sports betting software, and it has been grown up until today to be unique in its coverage of the whole range of gaming products, including live casino, RNG casino, poker, bingo, and affiliate marketing solutions. Although his executive position impels primary occupation on management activities, the agile Wirex development process he built allows him to direct research and development of innovative solutions, and to put hands into each and every line of code in an efficient and effective manner. In the 1990s he was a researcher at Fondazione Ugo Bordoni, focusing his activity on computer security, particularly: role-based access control; SQL security; Java security; computer security evaluation criteria. He served as program committee member for ACM Workshop on Role-Based Access Control 1998, 1999, and 2000, and as a reviewer for 5th European Symposium on Research in Computer Security (ESORICS), 1998. He actively participated as Italian representative to ISO/IEC standardization committees, including: JTC1/SC32 Working Group 3 ?Database language - SQL?, 1997 and 1998, conceiving and writing the SQL:1999 standard new role-based security features; JTC1/SC27 Working Group 3 ?Evaluation Criteria for IT Security?, 1997, contributing with modularization of security criteria. Luigi Giuri graduated (summa cum laude) in Computer Science in 1991 at the University of Pisa (Italy). View more trainings by Luigi Giuri at https://www.parleys.com/author/luigi-giuri Find more related tutorials at https://www.parleys.com/category/developer-training-tutorials
Views: 683 Oracle Developers
The Web Fights Back Against Online Terrorism
 
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Facebook has teamed up with Twitter, YouTube and Microsoft to fight the proliferation of terrorist content on the Web. The tech giants will create a shared industry database of hashes for violent terrorist imagery, terrorist recruitment videos, or images they have removed from their services. They may use these shared hashes to help identify potential terrorist content on their platforms. Hashes to be shared will apply to content that's most likely to violate all the companies' content policies. "Each one of the companies that is part of this agreement has its own specific definitions, practices and processes in place for governments to make requests to them for user data and to remove content," YouTube explained in policy notes provided to TechNewsWorld by company rep Stephanie Shih. "Any such requests for information will be routed through each company to handle as they normally do per its individual policies and procedures." No personally identifiable information will be shared. There will be no automated takedowns of terrorism-related content. Each company will retain its own process for dealing with appeals against its removal of content. The four will apply their own transparency and review practices when responding to any government requests. ISIS, or ISIL, has used the Web to great effect for the purpose of broadcasting its ideology and recruiting fighters, the UN Security Council's Counter-Terrorism Committee said last year, noting that it then had 30,000 fighters, drawn from more than 100 countries. All four of the tech participants that teamed in the latest initiative already have launched separate efforts to counter terrorist activities online, in some cases through other partnerships. "Our existing efforts to counter extremism and terrorist content will continue," Facebook said in comments provided to TechNewsWorld by spokesperson Alec Gerlach. "This agreement means that there will be more operational efficiency as we try and stop terrorist content from easily migrating between platforms." Twitter earlier this year outlined its policy, which includes deactivating accounts linked to terrorism groups, cooperating with law enforcement entities when appropriate, and partnering with organizations working to counter extremist content online. Facebook earlier this year began offering advertising credits to some users combating terrorism online, and it began collaborating with the U.S. State Department to develop antiterrorist messaging from college students. YouTube's content policies strictly prohibit terrorist recruitment and content intending to incite violence, the company said. YouTube terminates any account if it has reason to believe that the account holder is an agent of a foreign terrorist organization. Google parent company Alphabet this summer partnered with Facebook and Twitter to sponsor three experiments using videos to combat the spread of terrorist propaganda on their sites. Google think tank Jigsaw this summer launched Redirect, a pilot project that aims to redirect people searching for jihadist information online toward counterterrorism content. Project Redirect is not involved in YouTube's partnership with Microsoft, Facebook and Twitter. Microsoft this spring outlined its two-pronged approach to the online terrorism problem: addressing the appearance of related content on its services; and partnering with others to tackle the issue more broadly. http://www.technewsworld.com/story/84148.html?rss=1 http://www.wochit.com This video was produced by YT Wochit Tech using http://wochit.com
Views: 68 Wochit Tech
Facebook CEO Mark Zuckerberg testifies on data scandal for a 2nd day before Congress
 
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Facebook CEO Mark Zuckerberg faces a second a day of testimony in front of the House energy and commerce committee amid concerns over privacy on the social media site. It was revealed Facebook shared the information of 87 million users with data giant Cambridge Analytica. To read more: http://cbc.ca/ »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 17793 CBC News
Big Data Analytics on Amazon Web Services (AWS)
 
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Learn more about Big Data Analytics on AWS here - http://amzn.to/2naCrFJ Building or expanding your own data center to handle big data workloads is costly, takes a long time, and doesn’t allow your business to move fast enough. The cloud can help with a lot of these problems. Using the cloud to store and process your data can significantly reduce the cost, scalability, and elasticity issues of managing your own data center. But just deciding to move to the cloud isn’t going to solve your big data problems overnight. The problem is, lots of cloud providers offer just a subset of everything you need. AWS is different. AWS provides the broadest platform for big data analytics in the market today, with deep and rapidly expanding functionality across big data stores, data warehousing, distributed analytics, real-time streaming, machine learning, and business intelligence. These building blocks – along with capabilities to meet the strictest security requirements – allow customers to quickly and easily tackle a wide range of analytics challenges. No other platform provides this level of depth. That’s why leading organizations like GE, J&J, Philips, Nasdaq, Netflix, Airbnb, Pinterest, as well as many others across every industry, are currently using AWS to run their big data analytic workloads.
Views: 8903 Amazon Web Services
David Wilcock | Corey Goode: The Antarctic Atlantis [MUST SEE LIVE DISCLOSURE!]
 
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Are we about to hear that ancient ruins have been found in Antarctica? Is there an Alliance working to defeat the greatest threat humanity has ever faced on earth? Could the Antarctic Atlantis be part of a full or partial disclosure? Join David Wilcock on a thrill ride of discovery, beginning with Part One where he presents data on the Secret Space Program and shares the stage with legendary insider Corey Goode. This is the best public summary David and Corey have done of this amazing story that has captivated the UFO community. Part Two begins at the 53-minute mark, with David connecting the dots between intel from multiple insiders to arrive at a stunning conclusion -- that we are on the verge of major new releases of information that will transform everything we thought we knew about life on earth. A civilization of "Pre-Adamite" giants with elongated skulls appears to have crash-landed on a continent we now call Antarctica some 55,000 years ago. Various groups we collectively call the Alliance are working to defeat the Cabal / Illuminati / New World Order, thus making the headlines crazier by the day. If the Alliance succeeds, their plan is now to begin the disclosure process by telling us there was a civilization in Antarctica. We are already seeing multiple, compelling hints of this in corporate media. Find out what the insiders are telling us and help spread the word! This is a two-and-a-half-hour excerpt from David's Friday and Saturday presentations at the Conscious Life Expo 2017. In their original form they run six hours. David also spoke for three hours on Monday, presenting an incredible new model of the Cosmos based on sacred geometry -- and proving that the Sun is going to release a DNA-transforming burst of energy in our near future. Go to http://consciouslifestream.com to order the complete nine-hour set of videos, known as the David Wilcock Trilogy Pass. Reposting this video is stealing, so please share the link with your friends but do not re-upload it anywhere else. Our team does issue takedowns and it could lead to the loss of your channel. Please help us by subscribing to this channel! And make sure to check out David on Gaia at http://gaia.com/davidwilcock. You can see everything he has on the network, along with 7000 other unique metaphysical and Seeking Truth titles, for 99 cents in the first month. Lastly, sign up at http://dwilcock.com to be notified of new articles and videos upon release, and to get free gifts and Ascension updates from David as they become available. Thank you for your support!
Machine Reading with Word Vectors (ft. Martin Jaggi)
 
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This video discusses how to represent words by vectors, as prescribed by word2vec. It features Martin Jaggi, Assistant Professor of the IC School at EPFL. https://people.epfl.ch/martin.jaggi Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean (2013). Efficient Estimation of Word Representations in Vector Space. https://arxiv.org/pdf/1301.3781v3.pdf Omar Levy and Yoav Goldberg (2014). Neural Word Embedding as Implicit Matrix Factorization. https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf
Views: 14532 ZettaBytes, EPFL
Live: Mark Zuckerberg Testimony to Senate Judiciary and Commerce Committees
 
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Mark Zuckerberg is scheduled to appear before a joint hearing of the Senate Judiciary and Commerce Committees today.The hearing will focus on the use of and protection of Facebook user data. Follow CBS News live blog here: https://www.cbsnews.com/live-news/watch-mark-zuckerberg-testimony-senate-judiciary-commerce-committee-facebook-data-breach-today-live/ The public grilling, coming in the wake of the Cambridge Analytica scandal, could be a turning point for the social media behemoth and its young founder, which has largely avoided government regulation in its 15-year existence. On Monday, the House Committee on Energy and Commerce posted Zuckerberg's prepared testimony, in which the 33-year-old billionaire said his company needed to do more to protect the privacy of its users. "We didn't take a broad enough view of our responsibility and that was a big mistake," Zuckerberg said in the testimony. "It was my mistake, and I'm sorry. I started Facebook. I run it, and I'm responsible for what happens here." Subscribe to the CBS News Channel HERE: http://youtube.com/cbsnews Watch CBSN live HERE: http://cbsn.ws/1PlLpZ7 Follow CBS News on Instagram HERE: https://www.instagram.com/cbsnews/ Like CBS News on Facebook HERE: http://facebook.com/cbsnews Follow CBS News on Twitter HERE: http://twitter.com/cbsnews Get the latest news and best in original reporting from CBS News delivered to your inbox. Subscribe to newsletters HERE: http://cbsn.ws/1RqHw7T Get your news on the go! Download CBS News mobile apps HERE: http://cbsn.ws/1Xb1WC8 Get new episodes of shows you love across devices the next day, stream CBSN and local news live, and watch full seasons of CBS fan favorites like Star Trek Discovery anytime, anywhere with CBS All Access. Try it free! http://bit.ly/1OQA29B --- CBSN is the first digital streaming news network that will allow Internet-connected consumers to watch live, anchored news coverage on their connected TV and other devices. At launch, the network is available 24/7 and makes all of the resources of CBS News available directly on digital platforms with live, anchored coverage 15 hours each weekday. CBSN. Always On.
Views: 156458 CBS News
Mark Zuckerberg testifies on Capitol Hill (full Senate hearing)
 
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Live coverage and analysis of Facebook CEO Mark Zuckerberg's testimony before a joint hearing of the Senate Judiciary and Commerce committees. Read more: https://wapo.st/2HrQ2TB Subscribe to The Washington Post on YouTube: http://bit.ly/2qiJ4dy Follow us: Twitter: https://twitter.com/washingtonpost Instagram: https://www.instagram.com/washingtonpost/ Facebook: https://www.facebook.com/washingtonpost/
Views: 721303 Washington Post
Pedro Domingos: "The Master Algorithm" | Talks at Google
 
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Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and getting presidents elected. But there is a push to use machine learning to do even more—to cure cancer and AIDS and possibly solve every problem humanity has. Domingos is at the very forefront of the search for the Master Algorithm, a universal learner capable of deriving all knowledge—past, present and future—from data. In this book, he lifts the veil on the usually secretive machine learning industry and details the quest for the Master Algorithm, along with the revolutionary implications such a discovery will have on our society. Pedro Domingos is a Professor of Computer Science and Engineering at the University of Washington, and he is the cofounder of the International Machine Learning Society. https://books.google.com/books/about/The_Master_Algorithm.html?id=glUtrgEACAAJ This Authors at Google talk was hosted by Boris Debic. eBook https://play.google.com/store/books/details/Pedro_Domingos_The_Master_Algorithm?id=CPgqCgAAQBAJ
Views: 106889 Talks at Google
GMO's Revealed: Episode 1
 
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EPISODE 1 Dr. Zach Bush, Vani Hari, Gunnar Lovelace TOPICS: Glyphosate impacts and effects Changes in our food supply and the consequential domino effect on our health Innovations in food supply to avoid GMOs Register to watch GMOs Revealed Aug 22 - 30. http://gmosrevealed.com Each episode is up 24 hours for you to watch FREE, starting August 22nd We will be showing 9 episodes in 9 days.
Views: 291543 GMOs Revealed
Lecture 10 - Neural Networks
 
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Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 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 May 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 337425 caltech
This is How the Bull Market Ends
 
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Subscribe to stay up to date with the latest videos ► https://www.sbry.co/suBiH Episode 43 – This is How the Bull Market Ends Porter gives one last lesson on the big mistake Warren Buffett is making that could change Berkshire Hathaway forever. Buck breaks down the culture of the FBI and its influence on “Russiagate,” and Porter wonders why successful money managers and business people are often the target of politicians, while real criminals like those behind the 2008-2009 financial crisis just walk away. Buck and Porter welcome Erez Kalir, CEO and co-founder of Stansberry Asset Management. Porter and Erez discuss four specific “mile markers” to watch for that will signal the end of the current bull run in stocks. Erez talks about a meeting with Peter Thiel where the famous venture capitalist reveals what he’s doing with his bitcoin investment. What’s the one stock you would buy if it was the only one you could hold forever? Buck gives his account of the Facebook data breach that erased over $35 billion in investor capital in one day. Is the stock a buy or sell? Porter weighs in. A listener sends in a question about student debt forgiveness and what it would mean to Porter’s American Jubilee prediction. Be sure to click here to never miss an episode ↓ SPOTIFY ► https://www.sbry.co/ufnNP GOOGLE PLAY MUSIC ► https://www.sbry.co/lkwhp ITUNES ► https://www.sbry.co/7OQ79 SOUNDCLOUD ► https://www.sbry.co/jHn5h STITCHER ► https://www.sbry.co/tEkL5 Check out NewsWire’s Investors MarketCast ↓ GOOGLE PLAY MUSIC ► https://www.sbry.co/dzzKq APPLE ITUNES ► https://www.sbry.co/GoCV0 STITCHER ► https://www.sbry.co/s86p1 ———————————— Follow us on Twitter ► https://www.sbry.co/p11ih Join our Facebook Community ► https://www.sbry.co/fMckK Check out our website ► https://www.sbry.co/wUAye Check out Stansberry NewsWire ►https://www.sbry.co/IhNeW Check out Health and Wealth Bulletin ► https://www.sbry.co/iHRmD Check out Extreme Value ► https://www.sbry.co/EvIiH ———————————— SHOW HIGHLIGHTS: 3:28 Buck explains what Western journalists don’t understand about Putin or his unsurprising election victory last week, as well as the big reason he’s popular in Russia. 4:15 Porter immediately guesses why you won’t hear nearly as much about this week’s school shooting in Maryland as you did about last month’s shooting in Florida. 6:23 Porter goes into the scorching feedback he’s still getting after his criticism of Buffett – and the magazine that apparently liked his argument so much they reported a similar story. 10:15 Is the FBI just a tool for the Democratic Party at this point? Buck explains the schism between field agents and analysts in the Bureau, and the department we should be watching that’s “just to the right of Karl Marx.” 13:45 Porter says there’s no such thing as a predatory loan – no matter how sympathetic the “victims.” “When I started my business, I’d have been grateful for any predatory loan. (laughs)” 18:30 Before there was Satoshi Nakamoto, there was Milken – the man whose special class of junk bonds were like the Bitcoin of his day. Porter tells the story of why Rudy Giuliani couldn’t let his creation stand and brought the full force of his office down on Milken. 21:07 What do you have to do to earn 370 years of prison time? Buck explains the cloud that’s under Paul Manafort, and the charges he’s facing that carry a far more severe penalty than murder. 24:42 Buck introduces this week’s podcast guest Erez Kalir, who prior to launching Stansberry Asset Management co-founded and led Sabretooth Capital, with $1 billion in assets under management. 25:50 Erez shares his thoughts on the end of this bull market, and why the increase in volatility we’ve seen in 2018 so far is a warning sign. 28:50 Porter gets Erez’s take on the four developments he sees as being the most telling in the stock market today, from the decline of cryptos to the death of Toys R Us. 34:57 Porter explains the art of capital structure, and what it means to position your money based on your convictions. Hint: It goes way beyond just buying equities. 45:08 What to make of the collapse of the speculative bubble in cryptocurrencies – an asset class that didn’t even exist in the last bull market? Porter shares the dampening effect this could have on the stock market. “You’re unlikely to see stocks go up another 20%.” 57:09 Porter asks Erez to indulge in a mental exercise: You can only by one stock, so where do you choose to put all your wealth? “I’m pretty sure my great, great grandkids are going to be using salt.” 1:01:49 Buck describes the scandal surrounding Facebook and Cambridge Analytica, the insidious way they siphoned off people’s person information to a third party, and why it didn’t make a splash in 2012 when the Obama re-elect team openly bragged about this.
The Story of Stuff
 
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From its extraction through sale, use and disposal, all the stuff in our lives affects communities at home and abroad, yet most of this is hidden from view. The Story of Stuff is a 20-minute, fast-paced, fact-filled look at the underside of our production and consumption patterns. The Story of Stuff exposes the connections between a huge number of environmental and social issues, and calls us together to create a more sustainable and just world. It'll teach you something, it'll make you laugh, and it just may change the way you look at all the stuff in your life forever. http://storyofstuff.org And for all you fact checkers out there: http://storyofstuff.org/movies/story-of-stuff/ GET INVOLVED: http://action.storyofstuff.org/sign/social-action/ FOLLOW US: Facebook: https://www.facebook.com/storyofstuff/ Twitter: https://twitter.com/storyofstuff Instagram: https://www.instagram.com/storyofstuff/ SUPPORT THE PROJECT: https://action.storyofstuff.org/donate/social_donations/ Help us caption & translate this video! http://amara.org/v/BKO/
Dangerous Dealings | Critical Role RPG Show Episode 42
 
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Season 3 of Geek and Sundry Painters Guild is here! Come paint with us on Alpha, and use the code MINI for a free 60-day trial: www.projectalpha.com Check out our store for official Critical Role merch: https://goo.gl/BhXLst Catch Critical Role live Thursdays at 7PM PT on Alpha and Twitch: Alpha: https://goo.gl/c4ZsBj Twitch: https://goo.gl/D9fsrS Listen to the Critical Role podcast: https://goo.gl/jVwPBr In the wake of the devastation of Emon, Vox Machina begin making their long term plans to take back the city. But first, they must decide whether or not to make a devil's bargain with the Clasp: a dangerous underground criminal organization. For more on RPGs, go to http://bit.ly/GS_RPG Visit us on http://geekandsundry.com Subscribe to Geek and Sundry: http://goo.gl/B62jl Join our community at: http://geekandsundry.com/community Twitter: http://twitter.com/geekandsundry Facebook: http://facebook.com/geekandsundry Instagram: http://instagram.com/geekandsundry Google+: https://plus.google.com/+GeekandSundry/
Views: 1025318 Geek & Sundry
The World In 2050 [The Real Future Of Earth] - Full BBC Documentary HD
 
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The World In 2050 [The Real Future Of Earth] - Full BBC Documentary HD ✳️ Visit: https://lixtle.com/batteryfix and discover a simple method to bring any dead battery back to life again (car batteries, computer and phone batteries, and many other types of batteries), just like new. Description of the video: Can you imagine our world in 2050? By mid-century there will likely be 9 billion people on the planet, consuming ever more resources and leading ever more technologically complex lives. What will our cities be like? How will we eat in the future of Earth? Will global warming trigger catastrophic changes, or will we be able to engineer our way out of the world climate crisis? In the future world demographic changes will certainly be dramatic. Rockefeller University mathematical biologist Joel Cohen says it's likely that by 2050 the majority of the people in the world will live in urban areas of the earth, and will have a significantly higher average age than people today. Recommended video: Skyrocket your metabolism: https://www.youtube.com/watch?v=c7xD_g1T4pE We generally publish: BBC Documentaries HD, History Channel Documentaries, National Geographic and Discovery Channel Documentaries. Video Details: The World In 2050 [The Real Future Of Earth] - Full BBC Documentary HD https://www.youtube.com/watch?v=g_1oiJqE3OI Follow us on YouTube: https://www.youtube.com/c/TopClassDocumentariesHD Twitter: https://twitter.com/kodeykon
Views: 1766127 Top Class Documentaries
2015 openstack vancouver - Aparupa Das Gupta – Anomaly Detection for Machine Generated Big Data
 
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Machine generated data in the form of logs and metrics play a key role in performing functionalities related to health-checks, security, and performance of any organization’s applications, operations, and business infrastructure. However, organizations are struggling to leverage the vast amount of data that is getting generated at an explosive rate. One of the key insights that can be generated from these vast amounts of data is in the area of Anomaly Detection. Using statistical and machine learning methods, Anomaly Detection allows us to identify anomalies in streams of machine data. This can be useful in multitude of ways, for instance, identifying potential failures in storage drives or breach of security within applications and services. In this talk we will present (i) an intimate view of what machine generated streaming data looks like, (ii) what are the different broad categories in which you can classify these data based on their characteristics (example: periodic metrics, non-periodic metrics), (iii) the state-of-the-art approaches available to address anomaly detection for each such category of data, and (iv) the ongoing challenges in the field of anomaly detection for machine generated streaming data.
Views: 124 vBrownBag
Cryptobay 2018: Best altcoins to invest, mine, and research
 
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Altcoin, the abbreviation for alternative coin, or alternate coin, or all blockchain projects and related currency that exist in addition to Bitcoin. Some random facts: The first altcoin is Namecoin, there are 1325+ altcoins, basically every coin created after Bitcoin can be called an altcoin. Yes, even Ethereum, Ripple or Dash are altcoins. Connect with us and get your THAT COINS! Telegram group: t.me/cryptobayto Youtube:bit.ly/watchcryptobay Meetup: bit.ly/Crypto-Bay Get THAT Coins: bit.ly/THATbounty New altcoins: 70 +5% New markets (pairs): 804 Market cap: $742B-$358B=$384B +107% Bitcoin dominance: 33.8% vs 55.7% Vaporware - software or hardware that has been advertised but is not yet available to buy, because its only in the concept or development phase. TRON is a world-leading blockchain-based decentralized protocol that aims to construct a worldwide free content entertainment system with the blockchain and distributed storage technology. Because TRON's goal is to completely revolutionize the outdated HTTP protocol, much of the coding has to be done from scratch. Genesis Date August 28, 2017 (4 months) MARKET CAPITALIZATION $9,151,183,446.06 Dec 4, 2017 $0.002 Jan 4, 2018 $0.23 Jan 9, 2018 $0.14 Verge - privacy Dogecoin. They fail to provide privacy, 2% of all addresses use TOR network. Genesis Date October 09, 2014 (over 3 years) MARKET CAP $3,145,676,252.79 Dec 4, 2017 $0.008 Dec 23, 2017 $0.26 Jan 9, 2017 $0.22 Cardano - upgraded Ethereum. The founder is the co-founder of Ethereum. Faster transactions, scalability, data compression. Not live yet. MARKET CAP $23,148,666,302.02 Nov 24, 2017 $0.03 Jan 3, 2018 $1.18 Jan 9, 2018 $0.89 XRP. Interbank transaction platform. Claim to have 100 banks on board, but none of them seem to use it. Dec 11, 2017 $0.25 Jan 6, 2018 $3.40 Jam 9, 2018 $2,17 NEM Proof-of-Importance, Multisignature accounts, Messaging Genesis Date March 31, 2015 MARKET CAP $14,433,592,683.95 Dec 4, 2017 $0.31 Jan 6, 2018 $1.87 Jan 9, 2018 $1.60 Altcoins short description Ethereum Problem it is aiming to solve: Shortest term: the need for a platform to issue ICO tokens (and sell them with smart contracts); mid term: legal contracts are inefficient; long term: AI overlords IOTA Problem: There needs to be a way for participating devices on the internet of things to communicate with each other, without fees. Dash Problem: Digital currencies should be consumer friendly, and there needs to be a process built into the network to allow it to fund both marketing and development of consumer friendly products. Ripple Problem it is aiming to solve: The banking system is disjointed and contains thousands of ledger systems spread across the world, it'd be better if they all were on the same ledger system; Market makers park insane amounts of capital to facilitate payments where on a more efficient system they wouldn't have to. Connecting all payment systems on one ledger, providing a blockchain system for banks to use. Litecoin Problem: Originally ASIC miners pushing out GPU miners from Bitcoin, recently, all the drama in Bitcoin is making people look for a backup Bitcoin. Ethereum Classic Problem: Ethereum shouldn't modify the network to refund losses to those who create faulty code. Code is law. Monero Problem: Payments on a decentralized network should be anonymized. Zcash Problem: Payments on a decentralized network should be anonymized. Waves Problem: ICOs on Ethereum are slow, they should happen on a faster, easier platform, with a decentralized exchange. Dogecoin Problem: Bitcoin just doesn't have enough memes. What happens when your dreams become memes. (One of the earlier 'joke' currencies, has a great community behind it) Cardano Improved Ethereum.
Views: 256 ThatChannel.com
Multimodal Interactions & JS: The What, The Why and The How by Diego Paez, Despegar
 
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Multimodal Interactions & JS: The What, The Why and The How - Diego Paez, Despegar The term 'multimodal interactions' (MMI) on the HCI field refers to the situation where a system offers many ways of interaction. This talk combines an academic subject with the daily JS we already know. It is an effort to bring together two, at first, different worlds looking for a win-win situation. On the one hand, academia could enjoy getting a novel approach to a particular problem thanks to our awesome JS. On the other, the industry can get new ways of interaction which can be applied on a variety of context and/or products. In short, the talk contains: - A quick introduction to MMI (Multimodal Interactions) - A particular novel approach to add support for MMI on Web Apps. - Short list of related open problems or possible research lines to pursuit. About Diego Paez Diego is a JS developer with a Computer Science degree and a passion for HCI. He was born in the *southernmost* place in America, **Tierra del Fuego**. He moved to La Plata where he got his degree at UNLP. Diego has co-founded LaPlataJS a local JS community and [GEUT](http://geutstudio.com/) a *mysterious* side collective project. He is currently working for Despegar, the biggest web travel agency in Latin America.
Views: 387 node.js
Bitcoin or Amazon? Self-employed or corporate welfare warehouse worker?
 
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The technical difficulties continue here in Spain, but I won't let them stop my Bitcoin videos. Every day baby! I posted this on Facebook earlier today. Please follow me on Twitter @TechBalt Change of format coming to this channel soon! All of you not from Baltimore get a peak into my Baltimore life in this video. Yesterday's show- https://www.youtube.com/watch?v=NDtJsfnfiKM Bitcoin people you need to know- https://www.youtube.com/watch?v=YuWlWnJqHn4 WHERE I AM STAYING IN ALBIR- https://www.airbnb.com/rooms/11549261 This Week in Bitcoin- https://www.youtube.com/watch?v=tHERi6eEbJw Thursday's show- https://www.youtube.com/watch?v=a6ZqPVunbMQ Tuesday's show- https://www.youtube.com/watch?v=nUPB7_gop9c Sunday's show- https://www.youtube.com/watch?v=J0HVah0Y1vo Broke the Bgold story here- https://www.youtube.com/watch?v=eAukxlpwyHg Birth of the Crypto-dividend- https://www.youtube.com/watch?v=rg86clOEd3A Email the Disrupt Meister intern- [email protected] and tell us why you want to be an intern! -------------------------------------------------------------------------------------- CryptoHWwallet affiliate link- https://www.cryptohwwallet.com?acc=a87ff679a2f3e71d9181a67b7542122c "MeisterFreeHW1Over200" This is the coupon that is for people who spends over $200 (exclude shipping) to get a free H.W1. Ledger hardware wallet. Limit 10 pcs only coupon use at first, first come first serve basis only. 1 per customer for up to 10 uses. "cryptoHWwalletTee" free Tshirt with no purchase necessary. buyers need to add the tshirt into the shopping cart, Apply coupon, it'll deduct $7.99 from total which leaves $5.00 to be paid for Shipping Shirts could run out so there might be shipping delays. . There are total 6 crptocurrencies. Bitcoin, Dash, Litecoin, Ethereum, Zcash and Monero. BUY Cryptograffiti shirts here and use the "Meister" discount code to get 10% off anything in the store! https://cryptograffiti.com/collections/all UPVOTE THIS- https://steemit.com/bitcoin/@bitcoinmeister/what-does-using-bitcoin-really-mean-crypto-dividend-update-bgold-bitcore Buy your Bitcoin Trezor storage device here: https://shop.trezor.io/?a=c81d29b7bbf1 Buy Bitcoin at Coinbase here: https://www.coinbase.com/join/528aa4ec443594782100003a CryptoHWwallet affiliate link- https://www.cryptohwwallet.com?acc=a87ff679a2f3e71d9181a67b7542122c Adam's Twitter- https://twitter.com/TechBalt Adam's Minds- https://www.minds.com/BitcoinMeister Support the cause if you like what I have to say: BTC: 3HZngc6ASzt3deDm582u8xJRFAwmz7YTwG ETC: 0xb28CD007E0495b34BA6030859030322b7bE8422B Monero: 47MnZvoKVeZL4xhczW3t7zTnHQhJ3wkJ2Yxgyh2iWKTDhqrvdxjg41xZXrJhzn4yXxGVCJyNBroxK738rHKfGPWkQRQ2jj1 LTC: LQm55H4oUCoVPiBd25A4v2jHXLtC9oo9Jg ETH: 0x0feb7bCd89C4Ea0c14FC7D94b9afBDE993034AD5 DASH: Xjcpo8Lh6NKQoV3F12pGpXUiK4XRoQyudN Decred: Dsoq2ZPcqQDj5TSBLMAFX2SxCMHaYFnDty4 I ACCEPT EVERY ALTCOIN! This video explains what to do to get me to list, talk about, and create a payment address for your favorite Altcoin: https://www.youtube.com/watch?v=VD9GOslS4zg Very Important Bitcoin and Altcoin storage video you need to watch- https://www.youtube.com/watch?v=aulSblKDeIU My latest Steemit post: https://steemit.com/bitcoin/@bitcoinmeister/tune-in-at-10-45am-est-this-week-in-bitcoin-10-6-2017-andy-hoffman-ansel-lindner-moneytrigz-crypto-dividends My Steemit page: https://steemit.com/@bitcoinmeister https://www.youtube.com/c/BitcoinMeister http://disruptmeister.com/ Value of every cryptocurrency- https://coinmarketcap.com Watch more of my Bitcoin videos here: https://www.youtube.com/playlist?list=PLLgyAakZPtCVQKl6naVHUfOiICFG8BYMp Adam Meister is available for an hour long Bitcoin/cryptocurrency consultation where he can walk you through the Trezor installation process and help you move your Bitcoins to the Trezor. He will answer all your questions in that hour. Trezor is just one Bitcoin topic that Adam can help you with, you can ask his advice on anything cryptocurrency related. From marketing and promotion to Altcoins to storage and the buy and hold philosophy. Adam's hourly rate is 0.11 Bitcoin. Feel free to email: Adam AT TrezorHelp DOT com to set up a Skype/phone consultation or to arrange an in person appearance or speaking engagement. Adam is available to speak at conferences around the world. Follow Adam on Twitter here: https://twitter.com/TechBalt Buy your Bitcoin Trezor storage device here: https://shop.trezor.io/?a=c81d29b7bbf1
Views: 1200 BitcoinMeister
Java Programming
 
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Cheat Sheet is Here : http://goo.gl/OPMjte Slower Java Tutorial : http://goo.gl/UHdlyP How to Install Java & Eclipse : http://goo.gl/vEEEJE Best Java Book : http://amzn.to/2l27h2h Support Me on Patreon : https://www.patreon.com/derekbanas In this Java programming Tutorial I'll teach you all of the core knowledge needed to write Java code in 30 minutes. This is the most popular request from everyone. I specifically cover the following topics: primitive data types, comments, class, import, Scanner, final, Strings, static, private, protected, public, constructors, math, hasNextLine, nextLine, getters, setters, method overloading, Random, casting, toString, conversion from Strings to primitives, converting from primitives to Strings, if, else, else if, print, println, printf, logical operators, comparison operators, ternary operator, switch, for, while, break, continue, do while, polymorphism, arrays, for each, multidimensional arrays and more.
Views: 3716987 Derek Banas
How to solve computer proxy server problem refusing connection while browsing internet in Firefox
 
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Remove the troubleshoot of proxy server refusing connecting problem in Firefox. Proxy server is a program or a server which comes in between the Client and the server. Proxy servers are mainly used for security purpose. You can be anonymous while visiting website. But sometimes if you enable proxy server in your internet connection in your LAN setting then your internet won't work. Learn how to remove this troubleshooting. Learn more at http://www.kundanstech.com Like my Facebook page at https://www.facebook.com/kundanstech
Views: 348302 Kundan Bhattarai
Machine Learning Documentary
 
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*****Short Documentary on Machine Learning****** When one comes across the word “Learn” the first thought that crosses the mind is the ability to observe, understand and draw inferences from any news, event, situation or activity and store it mentally and use it to resolve problems arising out of similar situations or experiences. For instance, when learning to drive one is taught the basic functions and usage of accelerator, clutch and brake and as one starts driving, one begins to learn when to use which of the 3 either alone or in combination. So as one comes across situations such as say traffic jams then one applies the learning that has been mentally noted in the past to navigate smoothly through a traffic jam. In due course of time, as one starts to gain expertise, one becomes adept at applying learning when confronted with situations. This process of drawing from previous experience and mapping it to present scenario and deciding the action to be taken happens in fraction of seconds. This whole process happens inadvertently and is called learning.
Views: 227 Education Channel
datascience@berkeley | Machine Learning at Scale
 
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This course builds on and goes beyond the collect-and-analyze phase of big data by focusing on how machine learning algorithms can be rewritten and extended to scale to work on petabytes of data, both structured and unstructured, to generate sophisticated models used for real-time predictions. Conceptually, the course is divided into two parts. The first covers fundamental concepts of MapReduce parallel computing, through the eyes of Hadoop, MrJob, and Spark, while diving deep into Spark Core, data frames, the Spark Shell, Spark Streaming, Spark SQL, MLlib, and more. The second part focuses on hands-on algorithmic design and development in parallel computing environments (Spark), developing algorithms (decision tree learning), graph processing algorithms (pagerank/shortest path), gradient descent algorithms (support vectors machines), and matrix factorization. Students will use MapReduce parallel compute frameworks for industrial applications and deployments for various fields, including advertising, finance, healthcare, and search engines. Examples and exercises will be made available in Python notebooks (Hadoop Streaming, MrJob and pySpark).
Views: 2998 [email protected]