59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 139206 Brandon Weinberg
Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label. To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 106892 sentdex
Hey everyone! Here’s an intro to techniques you can use to represent your features - including Bucketing, Crossing, Hashing, and Embedding - and utilities TensorFlow provides to help. Also included is a walkthrough of using TensorFlow Estimators to classify structured data. Links from the video: Code - https://goo.gl/K9dVqv Facets: https://goo.gl/Dfpb7W TensorFlow Embedding Projector: https://goo.gl/2SxrYK You can find Josh on Twitter: https://twitter.com/random_forests See Josh as a guest speaker in Week 2 of the openSAP course: https://goo.gl/UGGcX7 Thanks, and have fun! Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 96543 Google Developers
Random Forest - Fun and Easy Machine Learning ►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 ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ Hey Guys, and welcome to another Fun and Easy Machine Learning Algorithm on Random Forests. Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results. To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm. ------------------------------------------------------------ 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: 237233 Augmented Startups
Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up. Follow https://twitter.com/random_forests for updates on new episodes! Subscribe to the Google Developers: http://goo.gl/mQyv5L - Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG And here's our playlist: https://goo.gl/KewA03
Views: 1998543 Google Developers
Including Packages ===================== * 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: 35 Clickmyproject
하둡 기반의 Ankus (Data Mining and Machine Learning) 오픈소스의 전체 기능을 가이드한 동영상입니다.
Views: 418 전수현
Maxence Bernard, CEO of IO Square will speak at the 42nd international iDate Online & Mobile Dating Industry Conference & Summit. The event takes place October 14-16, 2015 a the Strand Palace Hotel in London, United Kingdom. Mr. Bernard will cover on Text Analytics and Machine Learning and how it applies to the dating industry. iDate is a business to business conference covering the global dating industry. The European conference, particularly the one in Great Britain, sheds insights for the CEO to the UK and Euro dating marketplace. iDate is attended by C-Level executives from the online dating, mobile dating, matchmaking, social discovery and social media segments. Each gain knowledge, insight and ultimately earn more revenue from the sessions and the networking the event provides. To see other videos about the event, click here: http://www.youtube.com/watch?v=BBiT1dPJjh4 http://www.youtube.com/watch?v=I9CB_HQM5Zs For more information see: http://www.internetdatingconference.com
Views: 109 Internet Dating Conference
This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 173101 Udacity
Full lecture: http://bit.ly/D-Tree A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Each split corresponds to a node in the. Splitting stops when every subset is pure (all elements belong to a single class) -- this can always be achieved, unless there are duplicate training examples with different classes.
Views: 533219 Victor Lavrenko
Access +100 programming courses in Zenva: https://academy.zenva.com/?zva_src=youtube In this course we’ll use Python to create an Artificial Intelligence (AI) that can determine when an incoming email is spam or not. The technique we’ll use to create this cool project is called Text Classification. The group of algorithms that we’ll cover and use is Naive Bayes. Using term frequency and inverse document frequency we’ll be able to tweak our AI for an improved accuracy. To build our AI we’ll use the publicly available Enron dataset. Our tutorial blogs: GameDev Academy: https://gamedevacademy.org HTML5 Hive: https://html5hive.org Android Kennel: https://androidkennel.org Swift Ludus: https://swiftludus.org De Idea A App: https://deideaaapp.org Twitter: @ZenvaTweets
Views: 3307 Zenva
In this video, we explore the original meaning of the biblical concept of “spirit” and what it means that God’s Spirit is personally present in all of creation. Ultimately, the Spirit was revealed through Jesus and sent out into the lives of his followers to bring about the new creation.
Views: 1543477 The Bible Project
Explore how to perform feature engineering, a technique for transforming raw data into features that are suitable for a machine learning algorithm. - MATLAB for Machine Learning: https://bit.ly/2tUPS0O - Try it now in your browser: https://bit.ly/2IS82KT Feature engineering starts with your best guess about what features might influence the action you’re trying to predict. After that, it’s an iterative process where you create new features, add them to your model, and see if your results have improved. This video provides a high-level overview of the topic, and it uses several examples to illustrate basic principles behind feature engineering and established ways for extracting features from signals, text, and images. -------------------------------------------------------------------------------------------------------- Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Views: 1418 MATLAB
Speaker: Rick Lawrence, Senior Manager, Machine Learning & Decision Analytics at IBM Research U-report is an open-source SMS platform operated by UNICEF Uganda, designed to give community members a voice on issues that impact them. Data received by the system are either SMS responses to a poll conducted by UNICEF or unsolicited reports of problems occurring anywhere within Uganda. There are currently 300,000 U-report participants, and they can send up to 10,000 unsolicited text messages a week. The objective of the program in Uganda is to understand the data in real-time, and have issues addressed by the appropriate department in UNICEF in a timely manner. This talk describes an automated message-understanding and routing system deployed by IBM and UNICEF in Uganda. We discuss a dual-supervision machine learning approach to leverage human-generated labels on both features and text examples, and conclude with a discussion of the societal impact that U-report is driving in Uganda.
Views: 319 IBM Research
The details of course are available here: https://cloudxlab.com/course/specialization/3/big-data-with-hadoop-and-spark (KnowBigData.com's is now CloudxLab.com) Subscribe to our channel for latest videos - https://www.youtube.com/channel/UC8mJ6DL1Q32UWyJUceoO8Jw This is the Introductory session on Machine learning with Mahout. It clears a lot of myths and confusion about Machine learning with Mahout. How exactly Mahout helps to build recommendations. This is a part of Hadoop and Big Data course. Our full Hadoop and Big Data course consists of Introduction of Hadoop and Big Data,HDFS architecture ,MapReduce ,YARN ,PIG Latin ,Hive,HBase,Mahout,Zookeeper,Oozie,Flume,Spark,Nosql with quizzes and assignments. More details below: - - - - - - - - - - How this works - 1. Our classes are conducted live online by our instructors via webinar or hangout. These are not pre-recorded classes. The instructor delivers the class using presentations, collaborative drawing tools, screenshares. 2. Every class is recorded, complete with the screen and the audio, and uploaded to the Learning Management System which is accessible to our attendees for life. 3. At the end of each session, assignments are provided which the attendees have to submit in the LMS. The assignments are continuously reviewed by our instructors and teaching assistants. In case we conclude that an attendee requires extra detailing, we schedule extra one-on-one sessions with that attendee. 5. After all sessions are over, we ask for the student's preference for a project. We form teams of 3-4 members and based on their interests we assign a project to each team. A project is usually of three weeks duration. If a team has an idea it wants to work on as a project, we screen the idea and the team can work on it, or we assign a project from the industry. 6. Based on your performance in Quizzes, Assignments and Projects, we provide the certificate and LinkedIn Recommendation, we will endorse you with tags such as Hadoop, Big Data. - - - - - - - - - - About the Our Big Data and Hadoop Course Our Big Data and Hadoop course is designed to impart knowledge, skills and hands on experience required to become a successful Hadoop Developer, Administrator or Tester. Concepts Covered: Big Data, NoSQL, Streaming, Analytics Tools Covered: HDFS, MapReduce, Pig, Hive, HBASE, Zookeeper, Flume, Sqoop, Oozie, Spark, Storm, Mahout - - - - - - - - - - What makes this Course unique - Interactive Classes: More Questions. Less Lectures. Simple explanations to complex topics by industry experts Hands on workshops and real life projects. Quizzes & Assignments Certificate of Course at the end of course A real life project involving Hadoop Lifetime access to course content Cloud Labs™ - Access to the cloud infrastructure if learners don't wish to install Hadoop on their computers - - - - - - - - - - What are the prerequisites to join Big Data and Hadoop course? To be able to take maximum benefit out of this course, you should have knowledge of the following: 1. Basics of SQL. 2. A know-how of the basics of programming. We will be providing video classes covering the basics of Java and Python. - - - - - - - - - - - - - Why Learn Big Data and Hadoop? Big Data is a collection of massive and complex data sets that are very difficult to manage and process with the existing tools intended for that purpose. Data generation is becoming a more obvious result of our everyday devices becoming cheaper, more powerful, compact and connected. We are generating data all the time such as tweeting, using emails, using facebook, uploading photos etc. Similarly our devices are also connected and are generating data. The result is a gargantuan mass of data that needs to be looked at for informed decision making. The only way ahead for organizations is to be able to store and process such large amounts of data, and for which, they use Big Data platforms like Hadoop. That proves the high demand of Hadoop Developers, Administrators, Tester and Scientists. The other way to measure the demand for Big Data and Hadoop technologies is to look at the number of jobs being posted around the world on these technologies. Also, Big Data features in the top #3 technology trends in organizations as per Forbes and Gartner. - - - - - - - - - - - - - - Please visit https://cloudxlab.com/course/1/big-data-with-hadoop-spark for more details and upcoming classes. For any queries call us at: +91 (80) 492-022-24 - IN / +1 (412) 568-3901 - US or write us at: [email protected] Post your questions on our forum - https://discuss.cloudxlab.com/
Views: 4188 KnowBigData
The objective in extreme multi-label classification is to learn a classifier that can automatically tag a data point with the most relevant subset of labels from a large label set. Extreme multi-label classification is an important research problem since not only does it enable the tackling of applications with many labels but it also allows the reformulation of ranking and recommendation problems with certain advantages over existing formulations. Our objective, in this talk, is to develop an extreme multi-label classifier that is faster to train and more accurate at prediction than the state-of-the-art Multi-label Random Forest (MLRF) algorithm [Agrawal et al. WWW 13] and the Label Partitioning for Sub-linear Ranking (LPSR) algorithm [Weston et al. ICML 13]. MLRF and LPSR learn a hierarchy to deal with the large number of labels but optimize task independent measures, such as the Gini index or clustering error, in order to learn the hierarchy. Our proposed FastXML algorithm achieves significantly higher accuracies by directly optimizing an nDCG based ranking loss function. We also develop an alternating minimization algorithm for efficiently optimizing the proposed formulation. Experiments reveal that FastXML can be trained on problems with more than a million labels on a standard desktop in eight hours using a single core and in an hour using multiple cores.
Views: 1996 Microsoft Research
A reflection on my changing views on Islam — and the ex-Muslims and Muslims who changed them. You can support the channel at: https://www.patreon.com/TheraminTrees Regarding Islam I'd also like to recommend Reason on Faith's 2019 video 'Why I am Leaving Islam and Ahmadiyyat' https://www.youtube.com/watch?v=4-jPHr0LrYY -- Arabic subtitles: TranquilOblivion Slovak subtitles: Peter Ščigulinský -- Council of Islamic Ideology quote: https://tribune.com.pk/story/1110571/name-protection-cii-bill-proposes-curbs-women/ -- Hadith quoted regarding homosexuality: https://sunnah.com/abudawud/40/112 -- Shia cleric Farrokh Sekaleshfar interview: https://www.youtube.com/watch?v=1rZhopaBPGo -- Hadith quoted regarding killing apostates: https://sunnah.com/bukhari/88/5 -- Former Grand Mufti of Egypt Ali Goma’a quotes: http://www.middle-east-online.com/english/?id=21562 http://gulfnews.com/news/mena/egypt/top-cleric-denies-freedom-to-choose-religion-comment-1.191048 -- Tarek Fatah quote, from interview: https://www.youtube.com/watch?v=VkefdZkP5mk -- Raheel Raza Story: http://www.independent.co.uk/news/uk/home-news/first-woman-to-lead-friday-prayers-in-uk-1996228.html Quote: https://www.youtube.com/watch?v=alkCxTHWa_I -- Asra Nomani Story: http://www.nytimes.com/2004/07/22/us/muslim-women-seeking-a-place-in-the-mosque.html?_r=0 http://www.washingtonpost.com/wp-dyn/content/article/2005/06/04/AR2005060401646.html Quote: https://www.youtube.com/watch?v=lYykIgZj70M -- Usama Hasan Story: https://www.theguardian.com/world/2011/mar/06/usama-hasan-london-imam-death-threats-evolution Quote: http://www.prospectmagazine.co.uk/politics/charlie-hebdo-attacks-time-for-reform-within-islam-shootings-paris -- Majjid Nawaz Some attempts to invite agreement on reform: https://www.youtube.com/watch?v=rpSHDaMLsnY https://www.youtube.com/watch?v=krXVn6APgoA Quote: http://www.npr.org/templates/transcript/transcript.php?storyId=377442344 -- Quotes by other ex-Muslim and Muslim activists: Nonie Darwish https://www.youtube.com/watch?v=8P-bgEkTel4 Sarah Haider https://www.youtube.com/watch?v=0plC24YuoJk Tawfik Hamid https://www.youtube.com/watch?v=ALoyvS8VZFA Salim Mansur https://www.youtube.com/watch?v=8fpfxKvMycM Wafa Sultan https://www.youtube.com/watch?v=nOK-F79aRBo Ibn Warraq https://www.youtube.com/watch?v=i4aY6kEO3cw -- Pew research http://www.pewforum.org/2015/04/02/muslims/pf_15-04-02_projectionstables74/ http://www.pewforum.org/2013/04/30/the-worlds-muslims-religion-politics-society-beliefs-about-sharia/ -- British Muslim apostasy question from Populus, ‘Living apart together’ poll: http://www.populus.co.uk/wp-content/uploads/2016/01/download_pdf-131206-Policy-Exchange-Poll-of-Muslims-Living-Apart-Together.pdf BBC article: http://news.bbc.co.uk/1/hi/6309983.stm -- Hadith quote regarding 73 sects https://sunnah.com/abudawud/42/1 -- Ahmadi Passport declaration article: http://news.bbc.co.uk/1/hi/programmes/from_our_own_correspondent/8744092.stm Persecution: https://www.channel4.com/news/hate-crime-investigation-into-threats-against-ahmadi-muslims https://www.theguardian.com/commentisfree/belief/2009/dec/08/ahmadi-mosque-walsall-protests Our’an commentary reference on friendship between believers and disbelievers: https://www.alislam.org/quran/tafseer/?page=2598®ion=E1 https://archive.org/details/AhmadiyyaCommentaryOfQuran ReasonOnFaith critique of Ahmadi social attitudes: http://reasononfaith.org/ahmadiyya-beliefs-and-practice/#Homosexuality — Quranists persecution articles: https://www.bloomberg.com/news/articles/2015-12-03/sudan-tries-27-on-apostasy-charge-that-may-bring-death-sentences http://www.sudantribune.com/spip.php?article51185 -- Sufi persecution articles: https://www.theguardian.com/commentisfree/belief/2010/may/10/islam-sufi-salafi-egypt-religion http://world.time.com/2012/07/02/timbuktus-destruction-why-islamists-are-wrecking-malis-cultural-heritage/#ixzz2Mn8L8Wh5 http://www.nytimes.com/2010/07/03/world/asia/03pstan.html -- Apostasy trials Adbul Rahman article: http://news.bbc.co.uk/1/hi/world/south_asia/4841334.stm Meriam Ibrahim https://www.theguardian.com/world/2015/feb/02/meriam-ibrahim-pregnant-death-row-sudan-your-questions -- Miscellaneous Muslims debate punishment for apostates https://www.youtube.com/watch?v=dbZy9hieNFQ Ex-Muslims discuss doublethink https://www.youtube.com/watch?v=qVvlzJaDll0 Some discussions on Qur’an 4:34 regarding beating women: https://www.youtube.com/watch?v=HWCkEZlu3mM https://www.youtube.com/watch?v=qJ_HHZvt31U https://www.youtube.com/watch?v=0bFVAlij_Xw https://www.youtube.com/watch?v=JCuBEqytt4U
Views: 123995 TheraminTrees
Google Tech Talk May 5, 2010 ABSTRACT Presented by Justin Ma. We explore online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. We show that this application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the distribution of features that typify malicious URLs is changing continuously. Using a real-time system we developed for gathering URL features, combined with a real-time source of labeled URLs from a large Web mail provider, we demonstrate that recently-developed online algorithms can be as accurate as batch techniques, achieving daily classification accuracies up to 99% over a balanced data set. Slides: http://cseweb.ucsd.edu/~jtma/google_talk/jtma-google10.pdf Justin Ma is a PhD candidate at UC San Diego advised by Stefan Savage, Geoff Voelker and Lawrence Saul. His research interests are in systems and networking with an emphasis on network security, and his current focus is the application of machine learning to problems in security. He will be joining UC Berkeley as a postdoc after graduation. [Home page: http://www.cs.ucsd.edu/~jtma/ ]
Views: 10548 GoogleTechTalks
Cloud AutoML is a suite of Machine Learning products that enables developers with limited machine learning expertise to train high quality models by leveraging Google’s state of the art transfer learning, and Neural Architecture Search technology. AutoML Vision is the first product to be released. It is a simple, secure and flexible ML service that lets you train custom vision models for your own use cases. Soon, Cloud AutoML will release other services for all other major fields of AI. Product page: https://cloud.google.com/automl/ Blog post: https://www.blog.google/topics/google-cloud/cloud-automl-making-ai-accessible-every-business/ Podcast: https://www.gcppodcast.com/post/episode-109-cloud-automl-vision-with-amy-unruh-and-sara-robinson/ Using Distributed TensorFlow with Cloud ML Engine and Cloud Datalab → http://bit.ly/2ILCe5b
Views: 92267 Google Cloud Platform
DOTNET PROJECTS,2013 DOTNET PROJECTS,IEEE 2013 PROJECTS,2013 IEEE PROJECTS,IT PROJECTS,ACADEMIC PROJECTS,ENGINEERING PROJECTS,CS PROJECTS,JAVA PROJECTS,APPLICATION PROJECTS,PROJECTS IN MADURAI,M.E PROJECTS,M.TECH PROJECTS,MCA PROJECTS,B.E PROJECTS,IEEE PROJECTS AT MADURAI,IEEE PROJECTS AT CHENNAI,IEEE PROJECTS AT COIMBATORE,PROJECT CENTER AT MADURAI,PROJECT CENTER AT CHENNAI,PROJECT CENTER AT COIMBATORE,BULK IEEE PROJECTS,REAL TIME PROJECTS,RESEARCH AND DEVELOPMENT,INPLANT TRAINING PROJECTS,STIPEND PROJECTS,INDUSTRIAL PROJECTS,MATLAB PROJECTS,JAVA PROJECTS,NS2 PROJECTS, Ph.D WORK,JOURNAL PUBLICATION, M.Phil PROJECTS,THESIS WORK,THESIS WORK FOR CS
Views: 145 RANJITH KUMAR
Аннунаки и сотворение человека на Земле. Автор Георгий Бореев. Интересная История гуманоидных цивилизаций Земли. ✫Все видео от Домового:► https://www.youtube.com/watch?v=APxxBgjXfOs&list=PLjGYLaeyUrEMPJbQixizcLs9AGDRhX53z ✫Подписаться на канал:► https://www.youtube.com/user/bsubm2009?sub_confirmation=1 Пришельцы смогли поселиться на Земле только после того, как внесли изменения в свой генетический код, чтобы приспособиться к здешним условиям жизни. Атмосфера Земли в те далекие времена была в 8 раз плотнее сегодняшней и в два раза треугольней. Желтый солнечный свет еле пробивался сквозь коричневые испарения болот и синие тучи. Поэтому первые две тысячи лет лирианцы бегали друг за дружкой по земле в противогазах. Юмористическо-Фантастичекие отрывки из книги Георгия Бореева История гуманоидных цивилизаций Земли. Автор Георгий Бореев Источник: https://goo.gl/ERCRNs #АННУНАКИ #гуманоиды #Domovoi ★Я в Социальных сетях!↓★ Телеграм:► https://t.me/domovoi_a Мои плейлисты: Домовёнок - домашние работы и хозяйство: https://www.youtube.com/watch?v=bABrc9JD240&list=PLjGYLaeyUrEN4GS0SwObGBOwD3ZjFiyng Простая еда - простые рецепты приготовления еды: https://www.youtube.com/watch?v=4cReKV9dDi4&list=PLjGYLaeyUrEMT07_Rt1mSXWRPdwNKhGVi Простые программы - о программах и компьютерах: https://www.youtube.com/watch?v=bABrc9JD240&list=PLjGYLaeyUrEPbnFYK_5hA3C_YpMOnZovd Возрастные ограничения: +18
Views: 192 Domovoi о жизни без гламура
Alongside the new MacBook Air and iPad Pro, Apple also announced a new update to the Mac Mini and in this video, we unbox it and go hands on with this powerful little machine. read more - https://www.macrumors.com/2018/11/13/mac-mini-hands-on/
Views: 38872 MacRumors
Please support Peter Joseph's new, upcoming film project: "InterReflections" by joining the mailing list and helping: http://www.interreflectionsmovie.com LIKE Peter Joseph @ https://www.facebook.com/peterjosephofficial FOLLOW Peter Joseph @ https://twitter.com/ZeitgeistFilm * Sign up for TZM Mailing List: http://www.thezeitgeistmovement.com/ Sign up for the Film Series Mailing List: http://zeitgeistmovie.com/ This is the Official Online (Youtube) Release of "Zeitgeist: Moving Forward" by Peter Joseph. [30 subtitles ADDED!] On Jan. 15th, 2011, "Zeitgeist: Moving Forward" was released theatrically to sold out crowds in 60 countries; 31 languages; 295 cities and 341 Venues. It has been noted as the largest non-profit independent film release in history. This is a non-commercial work and is available online for free viewing and no restrictions apply to uploading/download/posting/linking - as long as no money is exchanged. A Free DVD Torrent of the full 2 hr and 42 min film in 30 languages is also made available through the main website [below], with instructions on how one can download and burn the movie to DVD themselves. His other films are also freely available in this format. Website: http://www.zeitgeistmovingforward.com http://www.zeitgeistmovie.com SUPPORT PETER JOSEPH (DONATIONS): http://zeitgeistmovie.com/torrents.html Release Map: http://zeitgeistmovingforward.com/zmap DVD: http://zeitgeistmovie.com/order.html Movement: http://www.thezeitgeistmovement.com Subtitles provided by Linguistic Team International: http://forum.linguisticteam.org/
Views: 25026582 TZMOfficialChannel
As a content creator, there is never enough storage space to go around but the LaCie d2 Professional aims to solve this problem with its massive 10TB of storage and fast transfer speeds! Check out our quick hands-on with this massive hard drive. LaCie d2 Professional - https://amzn.to/2Prb7jl
Views: 9535 MacRumors
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 28240 Audiopedia
Our method based on available features on URL and page contents without using the search engines such Google ets, to detect the phishing websites where our methodology target to extract the most number of features exist in literature then find the robust features that are not effected by concept drift this is to answer the question are there features can give the required accuracy when the training and testing data come from different times? as the phishers changes there tactics from time to time. After we find such features using machine learning algorithms such as Genetic Algorithm, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) to examine the performance and by applying classifier using Artificial Neural Network(ANN), Support Vector Machine (SVM) and Treefit Algorithm to decide which one give us the best performance . The performance analysis have to be done using software simulation such as the Accuracy ,Sensitivity and Selectivity and all parameters related to examine the performance using Matlab.
Views: 3343 VERILOG COURSE TEAM
På denne videoen viser jeg hvordan du kan strikke sokker på Prym Maxi strikkemølle. Disse sokkene har jeg tovet, men det er ikke nødvendig. Sokkene kan varieres med antall masker i både bredde og lengde, og dermed kan oppskriften lett endres slik at den passer både en større og mindre fot In this video I show you how to knit socks on a Prym Maxi knitting mill. The socks I have knitted is felted, but this is not necessary. You can easily add/remove stitches and rows to make the sock suitable for both smaller and bigger feets. Besøk gjerne bloggen min / Visit my blog http://www.tidtilovers.com
Views: 53676 Ingunn Hvattum
The rise of accessible digital collections coupled with the development of tools for processing and analyzing data has enabled researchers to create new models of scholarship and inquiry. The National Digital Initiatives team invites leaders and experts from organizations that are collecting, preserving and providing researcher access to digital collections as data to share best practices and lessons learned. This event will also highlight new collaborative initiatives at the Library of Congress that seek to enhance researcher engagement and the use of digital collections as data. Hashtag: #AsData Schedule: http://digitalpreservation.gov/meetings/dcs16.html
Views: 11477 LibraryOfCongress
Project Description: Throughout 2015, Hillary Clinton has been embroiled in controversyover the use of personal email accounts on non-government servers during her time as the United States Secretary of State. Thanks to the Freedom of Information Act, on Monday, August 31, 2015, the State Department released nearly 7,000 pages of Clinton's heavily redacted emails. John Montroy, Jake Lehrhoff and Chris Neimeth took those emails and wrangled them for your exploring pleasure. During our presentation they will cover the tasks involved with munging and analyzing this data, including NLTK, sentiment analysis, MYSQL, Python, Flask, an AWS instance and lots of elbow grease. The project output includes:- A dashboard to enable exploration of Hillary’s emails - Displays of topics by sender and recipient - Sentiment analysis of emails Speaker Bio: Chris Neimeth is a serial entrepreneur in the technology, media and entertainment businesses.Chris has served in various strategic roles: CEO of Salon Media Group Inc., President of IAC Partner Marketing, Executive Vice President of Ticketmaster, President/CEO of Real Media, Chief Commercial Officer of Daylife, Senior Vice President for The New York Times Company Digital, and founder of Grey Interactive.He has twice served as member of the Aspen Institute Forum on Communication and Society, and is a two time elected Director of the Interactive Advertising Bureau. Projects: http://blog.nycdatascience.com/uncategorized/mass-shootings-in-america/ Jake Lehrhoff is a man of many hats. For six years he taught middle school English and chaired the department at a school for children with moderate-to-severe emotional and behavioral disorders. He developed a system of intradepartmental supervision to monitor the efficiency and effectiveness of the billing department of a rheumatology laboratory. He wrote a novel about an autistic boy and edited the memoire of a triathlete. Jake holds a BA in psychology from Wesleyan University and an MA in psychology from Brandeis University, where he studied quantitative research methods and statistics and graduated with a perfect GPA. Jake takes great satisfaction in solving problems and is excited to apply his knowledge of machine learning and skills in R and Python to tackle new challenges. Blog: http://blog.nycdatascience.com/author/jake.lehrhoff/ John Montroy is a graduate of Middlebury College with a B.A. in Physics. After a summer of particle physics at CERN with the Harvard ATLAS team, he began his career as a data analyst in the auto industry. He has been programming since the age of 12, and delights in clean, re-usable, and functionally-oriented code. A self-starter and curious thinker, his interests run the gamut from mathematics to classical music. In his spare time, he can be found playing piano or mandolin, singing barbershop, and running. github: https://github.com/jmontroy90/teamhrc blog: http://blog.nycdatascience.com/author/jmontroy90/
Views: 387 NYC Data Science Academy
It's a beautiful day in this neighborhood. Gabe gives a little tour through Roblox's Welcome to Bloxburg. House tour, daily routine, working at the grocery market, house building, lounging at the beach and taking epic swim. [This is a slight re-edit/re-upload to fix copyright/contentID match issue] About Roblox: Roblox.com: "WHAT IS ROBLOX? ROBLOX is the best place to Imagine with Friends™. With the largest user-generated online gaming platform, and over 15 million games created by users, ROBLOX is the #1 gaming site for kids and teens (comScore). Every day, virtual explorers come to ROBLOX to create adventures, play games, role play, and learn with their friends in a family-friendly, immersive, 3D environment." Thanks for Watching another fun family friendly video! See you in the next video!!! https://www.youtube.com/c/KidMattersTV Subscribe for more, it's FREE! And Never Miss a video by Hitting that Bell Icon! ▶︎https://www.youtube.com/c/KidMattersTV?sub_confirmation=1 Watch More, from our Various Playlists: ▶︎https://www.youtube.com/c/kidmatterstv/playlists Follow Us On Social Media: ▶︎Twitter: https://twitter.com/KidMatters_TV ▶︎Facebook: https://www.facebook.com/kidmatterstv/ ▶︎Instagram: https://www.instagram.com/kidmatters_tv/ Fan Mail: KidMattersTV P.O. Box 1872 Manchester CT 06042-9998 Open Source Software we use: OBS Studio: https://obsproject.com GIMP (GNU Image Manipulation Program): https://www.gimp.org Blender (3D graphics and video editing): https://www.blender.org Audacity: https://www.audacityteam.org Handbrake: https://handbrake.fr OpenShot Video Editor: https://www.openshot.org About KidMatters+TV: KidMatters+TV is a Family Friendly Gaming Channel for everyone of all ages to enjoy! Primarily focused on family audience. Our videos are intended to be entertaining and educational for kids (family friendly/No swearing). Games, museums, adventures, crafts, experiments, toys, healthy cooking, and all things that matter to kids! We are a family of SEVEN: Gabe, Tragen, Roxy, Marcus, Hadrian, Mom, and Dad. The kids inspiration to start their own channel came from their favorite YouTubers (DanTDM, EthanGamer, Nerdy Nummies, Stampylongnose, Cookie Swirl C, and others). Important: This channel is edited and owned by the KidMatters+TV parents in accordance with YouTube rules. All communication will be monitored by their parents and any messages returned will be supervised by their parents. Contact Info: [email protected] KidMatters+TV [KM+Gaming S01E54] [CC] Closed Captions (Translations / Subtitles) are available in the following languages: Afrikaans Albanian Amharic Arabic Armenian Azerbaijani Bangla Belarusian Bosnian Bulgarian Catalan Chinese (Simplified) Chinese (Traditional) Croatian Czech Danish Dutch English Esperanto Estonian Filipino Finnish French Galician Georgian German Greek Gujarati Hausa Hebrew Hindi Hungarian Icelandic Igbo Indonesian Irish Italian Japanese Javanese Kannada Kazakh Khmer Korean Kurdish Kyrgyz Lao Latvian Lithuanian Macedonian Malagasy Malay Malayalam Maltese Marathi Mongolian Nepali Norwegian Pashto Persian Polish Portuguese (Brazil) Portuguese (Portugal) Punjabi Romanian Russian Scottish Gaelic Serbian Shona Sindhi Sinhala Slovak Slovenian Somali Southern Sotho Spanish Spanish (Latin America) Spanish (Mexico) Spanish (Spain) Sundanese Swahili Swedish Tajik Tamil Telugu Thai Turkish Ukrainian Urdu Uzbek Vietnamese Welsh Western Frisian Xhosa Yoruba Zulu Please HELP us have accurate Translations: This video: http://www.youtube.com/timedtext_video?ref=share&v=Rxqv8KaZhIU Our channel: http://www.youtube.com/timedtext_cs_panel?tab=2&c=UCkpFfjCLRUX9E62zJ1-r4Gg These were originally machine translated from the manually transcribed english file. So if you speak a foreign language, please help verify the accuracy of the closed captions/translations. Thanks for all your help.
Views: 9856 KidMattersTV
Psychological thriller: In which (until the often violent resolution) the conflict between the main characters is mental and emotional, rather than physical. Characters, either by accident or their own curiousness, are dragged into a dangerous conflict or situation that they are not prepared to resolve. Characters are not reliant on physical strength to overcome their brutish enemies, but rather are reliant on their mental resources, whether it be by battling wits with a formidable opponent or by battling for equilibrium in the character's own mind. At times, the characters attempt solving, or are involved in, a mystery. The suspense created by psychological thrillers often comes from two or more characters preying upon one another's minds, either by playing deceptive games with the other or by merely trying to demolish the other's mental state. The Alfred Hitchcock films Suspicion, Shadow of a Doubt, and Strangers on a Train and David Lynch's bizarre and influential Blue Velvet are notable examples of the type, as are The Talented Mr. Ripley, The Machinist, Don't Say A Word, House of 9, Trapped, Flightplan, Shutter Island, Secret Window, Identity, Red Eye, Phone Booth, Psycho, The River Wild, Nick of Time, P2, Breakdown, Panic Room, Misery, Straw Dogs and its remake, Cape Fear, The Collector, Frailty, The Good Son and Funny Games. Spy thriller: In which the protagonist is generally a government agent who must take violent action against agents of a rival government or (in recent years) terrorists. The subgenre usually deals with the subject of fictional espionage in a realistic way (such as the adaptations of John Le Carré). It is a significant aspect of British cinema, with leading British directors such as Alfred Hitchcock and Carol Reed making notable contributions and many films set in the British Secret Service. The spy film usually fuses the action and science fiction genres, however, some spy films fall safely in the action genre rather than thriller (e.i. James Bond), especially those having frequent shootouts, car chases and such (see the spy entry in the subgenres of action film). Thrillers within this subgenre include Spy Game, Hanna, Traitor, Tinker Tailor Soldier Spy, The Tourist, The Parallax View, The Tailor of Panama, Taken, Unknown, The Recruit, The Debt, The Good Shepherd and Three Days of the Condor. Supernatural thriller: In which the film brings in an otherworldly element (such as fantasy and/or the supernatural) mixed with tension, suspense and plot twists. Sometimes the protagonist and/or villain has some psychic ability and superpowers. Examples include, Lady in the Water, Fallen, Frequency, Next, Knowing, In Dreams, Flatliners, Jacob's Ladder, Chronicle, The Skeleton Key, What Lies Beneath, Unbreakable, The Gift, and The Dead Zone. Techno thriller: A suspense film in which the manipulation of sophisticated technology plays a prominent part. There is a bit of action and science fiction. Examples include The Thirteenth Floor, Jurassic Park, I, Robot, Eagle Eye, Hackers, The Net, Futureworld, eXistenZ and Virtuosity. Legal thriller: A suspense film in which in which the major characters are lawyers and their employees. The system of justice itself is always a major part of these works, at times almost functioning as one of the characters. Examples include, The Pelican Brief, Presumed Innocent, The Jury, The Kappa File, The Lincoln Lawyer, Hostile Witness and Silent Witness. http://en.wikipedia.org/wiki/Thriller_%28genre%29
Views: 144728 Remember This
This is the Midnight Ride with David Carrico on NYSTV with Jon Pounders talking about the true occult origins of the US. See whoever controls the narrative has spin control over the status quo. They decide what's moral or immoral. What's sane and insane. What's possible and not possible. And they especially decide the heros and the villians. You won't find this information on TV or in your history books. Very informative as always. Check out NYSTV, relevant talk. Not distractions like the rest of the media. I'm still trying to get these out as fast as possible, and there are a few errors on the subtitles towards the end in some languages. Sorry about that and I'll try to fix them as soon as I can. Afrikaans አማርኛ العربية Azərbaycanca / آذربايجان Boarisch Беларуская Български বাংলা བོད་ཡིག / Bod skad Bosanski Català Нохчийн Sinugboanong Binisaya ᏣᎳᎩ (supposed to be Burmese but it doesn't show...) Corsu Nehiyaw Česky словѣньскъ / slověnĭskŭ Cymraeg Dansk Deutsch Ελληνικά Esperanto Español Eesti Euskara فارسی Suomi Võro Français Frysk Gàidhlig Galego Avañe'ẽ ગુજરાતી هَوُسَ Hawai`i עברית हिन्दी Hrvatski Krèyol ayisyen Magyar Հայերեն Bahasa Indonesia Igbo Ido Íslenska Italiano 日本語 Basa Jawa ქართული Қазақша ភាសាខ្មែរ ಕನ್ನಡ 한국어 Kurdî / كوردی Коми Kırgızca / Кыргызча Latina Lëtzebuergesch ລາວ / Pha xa lao Lazuri / ლაზური Lietuvių Latviešu Malagasy 官話/官话 Māori Македонски മലയാളം Монгол Moldovenească मराठी Bahasa Melayu bil-Malti Myanmasa नेपाली Nederlands Norsk (bokmål / riksmål) Diné bizaad Chi-Chewa ਪੰਜਾਬੀ / पंजाबी / پنجابي Norfuk Polski پښتو Português Romani / रोमानी Kirundi Română Русский संस्कृतम् Sicilianu सिनधि Srpskohrvatski / Српскохрватски සිංහල Slovenčina Slovenščina Gagana Samoa chiShona Soomaaliga Shqip Српски Sesotho Basa Sunda Svenska Kiswahili தமிழ் తెలుగు Тоҷикӣ ไทย / Phasa Thai Tagalog Lea Faka-Tonga Türkçe Reo Mā`ohi Українська اردو Ўзбек Việtnam Хальмг isiXhosa ייִדיש Yorùbá 中文 isiZulu 中文(台灣) tokipona
Views: 26317 Free Truth Productions
The Great Gildersleeve (1941--1957), initially written by Leonard Lewis Levinson, was one of broadcast history's earliest spin-off programs. Built around Throckmorton Philharmonic Gildersleeve, a character who had been a staple on the classic radio situation comedy Fibber McGee and Molly, first introduced on Oct. 3, 1939, ep. #216. The Great Gildersleeve enjoyed its greatest success in the 1940s. Actor Harold Peary played the character during its transition from the parent show into the spin-off and later in a quartet of feature films released at the height of the show's popularity. On Fibber McGee and Molly, Peary's Gildersleeve was a pompous windbag who became a consistent McGee nemesis. "You're a haa-aa-aa-aard man, McGee!" became a Gildersleeve catchphrase. The character was given several conflicting first names on Fibber McGee and Molly, and on one episode his middle name was revealed as Philharmonic. Gildy admits as much at the end of "Gildersleeve's Diary" on the Fibber McGee and Molly series (Oct. 22, 1940). He soon became so popular that Kraft Foods—looking primarily to promote its Parkay margarine spread — sponsored a new series with Peary's Gildersleeve as the central, slightly softened and slightly befuddled focus of a lively new family. Premiering on August 31, 1941, The Great Gildersleeve moved the title character from the McGees' Wistful Vista to Summerfield, where Gildersleeve now oversaw his late brother-in-law's estate and took on the rearing of his orphaned niece and nephew, Marjorie (originally played by Lurene Tuttle and followed by Louise Erickson and Mary Lee Robb) and Leroy Forester (Walter Tetley). The household also included a cook named Birdie. Curiously, while Gildersleeve had occasionally spoken of his (never-present) wife in some Fibber episodes, in his own series the character was a confirmed bachelor. In a striking forerunner to such later television hits as Bachelor Father and Family Affair, both of which are centered on well-to-do uncles taking in their deceased siblings' children, Gildersleeve was a bachelor raising two children while, at first, administering a girdle manufacturing company ("If you want a better corset, of course, it's a Gildersleeve") and then for the bulk of the show's run, serving as Summerfield's water commissioner, between time with the ladies and nights with the boys. The Great Gildersleeve may have been the first broadcast show to be centered on a single parent balancing child-rearing, work, and a social life, done with taste and genuine wit, often at the expense of Gildersleeve's now slightly understated pomposity. Many of the original episodes were co-written by John Whedon, father of Tom Whedon (who wrote The Golden Girls), and grandfather of Deadwood scripter Zack Whedon and Joss Whedon (creator of Buffy the Vampire Slayer, Firefly and Dr. Horrible's Sing-Along Blog). The key to the show was Peary, whose booming voice and facility with moans, groans, laughs, shudders and inflection was as close to body language and facial suggestion as a voice could get. Peary was so effective, and Gildersleeve became so familiar a character, that he was referenced and satirized periodically in other comedies and in a few cartoons. http://en.wikipedia.org/wiki/Great_Gildersleeve
Views: 76815 Remember This