Home
Search results “Data mining ensemble classifiers gold”
Weka Tutorial 13: Stacking Multiple Classifiers (Classification)
 
08:52
In this tutorial I have shown how to use Weka for combining multiple classification algorithms. Both ensembles (bagging and boosting) and voting combining technique are discussed. The parameters and procedure to invoke stacking is left for the user because it is closely related to voting.
Views: 35421 Rushdi Shams
Naïve Bayes Classifier -  Fun and Easy Machine Learning
 
11:59
Naive Bayes Classifier- 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 -------------------------------------------------------------------------------- Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. ------------------------------------------------------------ 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: 185943 Augmented Startups
17. Learning: Boosting
 
51:40
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston Can multiple weak classifiers be used to make a strong one? We examine the boosting algorithm, which adjusts the weight of each classifier, and work through the math. We end with how boosting doesn't seem to overfit, and mention some applications. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 186781 MIT OpenCourseWare
Microsoft Excel Data Mining: Classification
 
06:49
Microsoft Excel Data Mining: Classification. For more visit here: www.dataminingtools.net
Model Comparisons in R
 
14:54
See the R code used to generate these results: require(fifer) data(relationship_satisfaction) d = relationship_satisfaction head(d) #### are we justified in adding interests, above and beyond communication and honesty? ### plot univariates flexplot(satisfaction~1, data=d) flexplot(interests~1, data=d) flexplot(honesty~1, data=d) flexplot(communication~1, data=d) ### plot model of interest for reduced model, but plot multiple ways flexplot(satisfaction~communication | honesty, data=d, method="lm", se=F, ghost.line="gray", ghost.reference=list(honesty=28)) flexplot(satisfaction~ honesty | communication, data=d, method="lm", se=F, ghost.line = "gray", ghost.reference = list(communication=28)) #### not much evidence of interactions/nonlinearity, so let's do an added variable added.plot(satisfaction~ communication + honesty, data=d, method="lm", se=F) added.plot(satisfaction~ honesty + communication, data=d, method="lm", se=F) #### now let's visualize the full model flexplot(satisfaction~ interests | honesty +communication, data=d, method="lm", se=F, ghost.line="gray", ghost.reference = list(honesty=23, communication = 28)) #### not drastically nonlinear/interaction, so let's do added variable plot added.plot(satisfaction~ honesty + communication + interests, data=d, method="lm", se=F) #### there's certainly something there! #### now let's look at diagnostics full = lm(satisfaction~honesty + communication + interests, data=d) reduced = lm(satisfaction~honesty + communication , data=d) visualize(full, plot="residuals") #### nothing too concerning, I reckon #### now let's look at model comparison estimates #source("research/RPackages/fifer/R/model.comparison.R") model.comparison(full, reduced) #### now we'll do a non-nested model comparison #### honesty versus interests, both controlling for communication #### and I'll skip to the added variable plot a = added.plot(satisfaction~ communication + interests, data=d, method="lm", se=F) b = added.plot(satisfaction~ communication + honesty, data=d, method="lm", se=F) cowplot::plot_grid(a,b) #### interests appears to be a stronger predictor mod1 = lm(satisfaction~ communication + interests, data=d) mod2 = lm(satisfaction~ communication + honesty, data=d) model.comparison(mod1, mod2)
Views: 58 Quant Psych
Two Effective Algorithms for Time Series Forecasting
 
14:20
In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting. Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz Join a community of over 250 K senior developers by signing up for InfoQ’s weekly Newsletter: https://bit.ly/2wwKVzu
Views: 57514 InfoQ
Machine Learning for Encrypted Malware Traffic Classification
 
02:36
Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity Blake Anderson (Cisco Systems, Inc.) David McGrew (Cisco Systems, Inc.) The application of machine learning for the detection of malicious network traffic has been well researched over the past several decades; it is particularly appealing when the traffic is encrypted because traditional pattern-matching approaches cannot be used. Unfortunately, the promise of machine learning has been slow to materialize in the network security domain. In this paper, we highlight two primary reasons why this is the case: inaccurate ground truth and a highly non-stationary data distribution. To demonstrate and understand the effect that these pitfalls have on popular machine learning algorithms, we design and carry out experiments that show how six common algorithms perform when confronted with real network data. With our experimental results, we identify the situations in which certain classes of algorithms underperform on the task of encrypted malware traffic classification. We offer concrete recommendations for practitioners given the real-world constraints outlined. From an algorithmic perspective, we find that the random forest ensemble method outperformed competing methods. More importantly, feature engineering was decisive; we found that iterating on the initial feature set, and including features suggested by domain experts, had a much greater impact on the performance of the classification system. For example, linear regression using the more expressive feature set easily outperformed the random forest method using a standard network traffic representation on all criteria considered. Our analysis is based on millions of TLS encrypted sessions collected over 12 months from a commercial malware sandbox and two geographically distinct, large enterprise networks. More on http://www.kdd.org/kdd2017/
Views: 1549 KDD2017 video
RapidMiner Tutorial (part 7/9) Naïve Bayes Classification
 
03:31
This tutorial starts with introduction of Dataset. All aspects of dataset are discussed. Then basic working of RapidMiner is discussed. Once the viewer is acquainted with the knowledge of dataset and basic working of RapidMiner, following operations are performed on the dataset. K-NN Classification Naïve Bayes Classification Decision Tree Association Rules
Views: 38838 RapidMinerTutorial
How Random Forest algorithm works
 
05:47
In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees. The presentation is available at: https://prezi.com/905bwnaa7dva/?utm_campaign=share&utm_medium=copy
Views: 325753 Thales Sehn Körting
Excel at Data Mining – Creating and Reading a Classification Matrix
 
05:29
In this video, Billy Decker of StatSlice Systems shows you how to create and read a Classification Matrix in 5 minutes with the Microsoft Excel data mining add-in*. In this example, we will create a Classification Matrix based on a mining structure with all of its associated models that we have created previously. For the example, we will be using a tutorial spreadsheet that can be found on Codeplex at: https://dataminingaddins.codeplex.com/releases/view/87029 You will also need to attach the AdventureworksDW2012 data file to SQL Server which can be downloaded here: http://msftdbprodsamples.codeplex.com/releases/view/55330 *This tutorial assumes that you have already installed the data mining add-in for Excel and configured the add-in to be pointed at an instance of SQL Server with Analysis Services to which you have access rights.
Views: 4352 StatSlice Systems
55 - Ensembling Tips and Tricks | How to Win a Data Science Competition: Learn from Top Kagglers
 
14:20
Lecture video from the course How to Win a Data Science Competition: Learn From Top Kagglers in the Advanced Machine Learning Specialization from the National Research University Higher School of Economics Download all the lecture notes of this course here: https://github.com/MrNewHorizons/StudyMaterials/tree/master/HowToWinDataScienceCompetition You can enroll in the course for a certificate here: https://www.coursera.org/learn/competitive-data-science
Views: 373 Hasan Shaukat
Latent Features - Intro to Machine Learning
 
00:25
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: 12338 Udacity
Semi-automated rainfall prediction models using Shiny
 
04:45
Here, I used Shiny, an R package that makes it easy to build interactive web applications (apps) straight from R, to develop semi-automated machine learning models to predict rainfall over a region the user selects. The user can extract predictand and predictors by drawing a polygon over a region. Then, the user can select some or all of the machine learning algorithms provided. Provided models include Linear regression models (GLM, SGLM), Tree-based ensemble models (random forest and boosting), Support vector Machines, Artificial Neural Network, and other non-linear models (GAM, SGAM, MARS). Finally, the user can download a presentation of the results.
Views: 1891 Fisseha Berhane
S2E2 of 5 Minutes with Ingo: Ensemble Methods
 
06:32
In this episode, you can see our chief-data-science-executive-wizard-commander Ingo and Data Scientist #7 ride around in a shopping cart. Of course, they also have to do something else as they eventually get tired of it. So, Ingo uses M&Ms to explain ensemble methods and how you can leverage the combined expertise of a panel of machine learning algorithms to deliver better predictive power. Watch Ingo ‘crowd guess’ the number of M&Ms in a bowl by random sampling strangers in a parking lot and averaging the estimates - exemplifying the bagging method. Also, learn how the boosting method differs from bagging (obviously using more M&Ms). Data Scientist #7 loves not only the M&Ms but also the idea of ensembles so he immediately wants to boost his knowledge by checking out the book: “Wisdom of Crowds” by James Surowiecki which Ingo recommended reading.
Views: 1076 RapidMiner, Inc.
A Hybrid Model for Gender Classification in Twitter
 
05:06
2017 Machine Learning Course Project
Views: 163 Liuqing Li
Build a TensorFlow Image Classifier in 5 Min
 
05:47
In this episode we're going to train our own image classifier to detect Darth Vader images. The code for this repository is here: https://github.com/llSourcell/tensorflow_image_classifier I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Challenge: The challenge for this episode is to create your own Image Classifier that would be a useful tool for scientists. Just post a clone of this repo that includes your retrained Inception Model (label it output_graph.pb). If it's too big for GitHub, just upload it to DropBox and post the link in your GitHub README. I'm going to judge all of them and the winner gets a shoutout from me in a future video, as well as a signed copy of my book 'Decentralized Applications'. This CodeLab by Google is super useful in learning this stuff: https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/?utm_campaign=chrome_series_machinelearning_063016&utm_source=gdev&utm_medium=yt-desc#0 This Tutorial by Google is also very useful: https://www.tensorflow.org/versions/r0.9/how_tos/image_retraining/index.html This is a good informational video: https://www.youtube.com/watch?v=VpDonQAKtE4 Really deep dive video on CNNs: https://www.youtube.com/watch?v=FmpDIaiMIeA I love you guys! Thanks for watching my videos and if you've found any of them useful I'd love your support on Patreon: https://www.patreon.com/user?u=3191693 Much more to come so please SUBSCRIBE, LIKE, and COMMENT! :) edit: Credit to Clarifai for the first conv net diagram in the video Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 705375 Siraj Raval
5. Building Decision Tree Models using RapidMiner Studio
 
18:44
This video describes (1) how to build a decision tree model, (2) how to interpret a decision tree, and (3) how to evaluate the model using a classification matrix.
Views: 21083 Pallab Sanyal
9 RapidMiner - Ensemble (Majority Vote)
 
21:45
Ensemble method เป็นเทคนิคที่ใช้ learning algorithms หลายตัวทำงานร่วมกัน เพื่อให้ได้ค่า predicted performance ที่สูงขึ้น คลิปนี้จะอธิบาย 1 ในหลายวิธีใน Ensemble method คือ Majority vote โดยใช้ RapidMiner
Views: 471 Kanda
Random Forest Classification in RStudio - Part 1
 
13:54
This video is the first of a three-part tutorial series on random forest classification in RStudio. In this video the example data set is taxpayer information and the real-life data problem we solve is can we classify and determine a person's political preference based on data attributes in a dataset? Link to dataset on Kaggle: https://www.kaggle.com/dmaillie/sample-us-taxpayer-dataset We start off doing a little data analysis in creating some new columns in our data set. Then we use the train function in caret package in r. We use the train function to build the random forest in rstudio. We then determine the accuracy of this model and then score the original data to see how accurate our predictions are. I show you how we go through this step-by-step in the code and then I also show you how I end up with some questions in the end that I want to answer and they'll be answered in the next videos. These questions are things like which attributes are stronger than others and can we make this random forest model more accurate? Random force models are easy to work with and can be very accurate. They are also a type of supervised learning as we are using this on data we already have. We just set the column of known values to knoll and then run our model to predict what they would be. We then placed the predicted values in a new column and compare them to what the old column was to see how accurate we really were. I hope you found this interesting and informational on how to use the caret package, train function and random forests in RStudio. Please stay tuned as I have two more videos in this series coming out and you will definitely want to watch those as I will show you how to determine which attributes are more important than others and then we will also look at increasing the accuracy of our model. Please subscribe, like and comment as I would love to hear from you. Thanks again have a great day. God bless!
Views: 342 Tech Know How
TTIC Distinguished Lecture Series - Geoffrey Hinton
 
01:08:08
Title: Dark Knowledge Abstract: A simple way to improve classification performance is to average the predictions of a large ensemble of different classifiers. This is great for winning competitions but requires too much computation at test time for practical applications such as speech recognition. In a widely ignored paper in 2006, Caruana and his collaborators showed that the knowledge in the ensemble could be transferred to a single, efficient model by training the single model to mimic the log probabilities of the ensemble average. This technique works because most of the knowledge in the learned ensemble is in the relative probabilities of extremely improbable wrong answers. For example, the ensemble may give a BMW a probability of one in a billion of being a garbage truck but this is still far greater (in the log domain) than its probability of being a carrot. This "dark knowledge", which is practically invisible in the class probabilities, defines a similarity metric over the classes that makes it much easier to learn a good classifier. I will describe a new variation of this technique called "distillation" and will show some surprising examples in which good classifiers over all of the classes can be learned from data in which some of the classes are entirely absent, provided the targets come from an ensemble that has been trained on all of the classes. I will also show how this technique can be used to improve a state-of-the-art acoustic model and will discuss its application to learning large sets of specialist models without overfitting. This is joint work with Oriol Vinyals and Jeff Dean. Bio: Geoffrey Hinton received his BA in experimental psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto where he is a University Professor. He is the director of the program on "Neural Computation and Adaptive Perception" which is funded by the Canadian Institute for Advanced Research. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. He is an honorary foreign member of the American Academy of Arts and Sciences, and a former president of the Cognitive Science Society. He has received honorary doctorates from the University of Edinburgh and the University of Sussex. He was awarded the first David E. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the IEEE Neural Network Pioneer award (1998), the ITAC/NSERC award for contributions to information technology (1992) the Killam prize for Engineering (2012) and the NSERC Herzberg Gold Medal (2010) which is Canada's top award in Science and Engineering. Geoffrey Hinton designs machine learning algorithms. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. His current main interest is in unsupervised learning procedures for multi-layer neural networks with rich sensory input.
Views: 27550 TTIC
Boston BSides - Machine Learning for Incident Detection - Chris McCubbin & David Bianco
 
43:06
Organizations today are collecting more information about what's going on in their environments than ever before, but manually sifting through all this data to find evil on your network is next to impossible. Increasingly, companies are turning to big data analytics and machine learning to detect security incidents. Most of these solutions are black-box products that cannot be easily tailored to the environments in which they run. Therefore, reliable detection of security incidents remains elusive, and there is a distinct lack of open source innovation. It doesn't have to be this way! Many security pros think nothing of whipping up a script to extract downloaded files from a PCAP, yet recoil in horror at the idea of writing their own machine learning tools. The "analytics barrier" is perceived to be very high, but getting started is much easier than you think! In this presentation, we’ll walk through the creation of a simple Python script that can learn to find malicious activity in your HTTP proxy logs. At the end of it all, you'll not only gain a useful tool to help you identify things that your IDS and SIEM might have missed, but you’ll also have the knowledge necessary to adapt that code to other uses as well. David J. Bianco is a Security Technologist at Sqrrl Data, Inc. Before coming to work as a Security Technologist and DFIR subject matter expert at Sqrrl, he led the hunt team at Mandiant, helping to develop and prototype innovative approaches to detect and respond to network attacks. Prior to that, he spent five years helping to build an intel-driven detection & response program for General Electric (GE-CIRT). He set detection strategies for a network of nearly 500 NSM sensors in over 160 countries and led response efforts for some of the company’s the most critical incidents. He stays active in the community, speaking and writing on the subjects of Incident Detection & Response, Threat Intelligence and Security Analytics. He is also a member of the MLSec Project (http://www.mlsecproject.org). You can follow him on Twitter as @DavidJBianco or subscribe to his blog, "Enterprise Detection & Response" (http://detect-respond.blogspot.com). Chris McCubbin is the Director of Data Science and a co-founder of Sqrrl Data, Inc. His primary task is prototyping new designs and algorithms to extend the capabilities of the Sqrrl Enterprise cybersecurity solution. Prior to cofounding Sqrrl, he spent 2 years developing big-data analytics for the Department of Defense at TexelTek, Inc and 10 years as Senior Professional Staff at the Johns Hopkins Applied Physics Laboratory where he applied machine learning algorithms to swarming unmanned vehicle ensembles. He holds a Masters degree in Computer Science and Bachelor’s degrees in Mathematics and Computer Science from the University of Maryland.
Views: 267 BSides Boston
Kaggle Cdiscount’s Image Classification Challenge — Pavel Ostyakov, Alexey Kharlamov
 
46:05
Pavel Ostyakov and Alexey Kharlamov share their solution of Kaggle Cdiscount’s Image Classification Challenge. In this competition, Kagglers were challenged to build a model that classifies the products based on their images. From this video you will learn: - How to decide which architectures to use - How to train networks faster - Problem with training second layer of classifiers - Errors while solving the problem - Ideas of other teams: using several images of product, ensembling and using kNN Павел Остяков и Алексей Харламов рассказывают про задачу классификации товаров по изображениям (Kaggle Cdiscount’s Image Classification Challenge). Павел и Алексей вместе со своей командой заняли в соревновании 5 место. Из видео вы сможете узнать: - Как принимается решение, какие архитектуры использовать - Способы ускорить обучение сетей - Сложности построения второго слоя классификаторов и способ решения - Ошибки, допущенные в процессе решения - Идеи других участников: использование нескольких изображений товара, ансамблирование и kNN Yandex hosts biweekly training sessions on machine learning. These meetings offer an opportunity for the participants of data analysis contests to meet, talk, and exchange experience. Each of these events is made up of a practical session and a report. The problems are taken from Kaggle and similar platforms. The reports are given by successful participants of recent contests, who share their strategies and talk about the techniques used by their competitors. On Dec. 9, we looked at Porto Seguro’s Safe Driver Prediction challenge on Kaggle.
Views: 2457 ML Trainings
RapidMiner Tutorial (part 6/9) K-NN Classification
 
05:18
This tutorial starts with introduction of Dataset. All aspects of dataset are discussed. Then basic working of RapidMiner is discussed. Once the viewer is acquainted with the knowledge of dataset and basic working of RapidMiner, following operations are performed on the dataset. K-NN Classification Naïve Bayes Classification Decision Tree Association Rules
Views: 32844 RapidMinerTutorial
Libor Mořkovský - Recognizing Malware (Machine Learning Prague 2016)
 
24:27
Recognizing Malware www.mlprague.com Slides: http://www.slideshare.net/mlprague/libor-mokovsk-recognizing-malware
Views: 179 Jiří Materna
Charles Martin: Can Machine Learning Apply to Musical Ensembles?
 
01:04
Part of the CHI 2016 Human Centred Machine Learning Workshop. The full paper is here: Can Machine-Learning Apply to Musical Ensembles? Charles Martin and Henry Gardner http://www.doc.gold.ac.uk/~mas02mg/HCML2016/HCML2016_paper_5.pdf
Views: 53 Marco Gillies
Kaggle Carvana Image Masking: определение фона на изображениях автомобилей — Сергей Мушинский
 
23:17
Сергей Мушинский рассказывает про задачу определение фона на изображениях автомобилей (Kaggle Carvana Image Masking Challenge). Сергей вместе со своей командой занял в соревновании 4 место. Из видео вы сможете узнать: - Использование псевдоразметки изображений нейронными сетями - Работа в команде в условиях общих ограниченных вычислительных ресурсов - Бывает ли полезно вручную доразмечать объекты - Подходы других участников: от двух сетей без усреднения до сложных ансамблей с разнообразными архитектурами Слайды: https://gh.mltrainings.ru/presentations/Mushinskiy_KaggleCarvanaImageMasking%20Challenge_2017.pdf Узнать о текущих соревнованиях можно на сайте http://mltrainings.ru/ Узнать о новых тренировках и видео можно из групп: ВКонтакте https://vk.com/mltrainings Facebook https://www.facebook.com/groups/1413405125598651/
Views: 3095 ML Trainings
The importance of the User Interface
 
04:28
I've created this how to guide to share with you the lessons that I learned when designing for Wordpress. This video is perfect for beginners and covers everything you should know when designing software. To learn more check out http://start2profit.com/user-interface-and-software-design
Views: 455 Start 2 Profit
Kaggle Camera Model Identification (1-2 places) — Artur Fattakhov, Ilya Kibardin, Dmitriy Abulkhanov
 
37:29
Artur Fattakhov, Ilya Kibardin and Dmitriy Abulkhanov share their winner’s solutions of Kaggle Camera Model Identification. In this competition, Kagglers challenged to build an algorithm that identifies which camera model captured an image by using traces intrinsically left in the image. From this video you will learn: - How to get additional photo data - Training Scheme with cyclic learning rate and pseudo labeling - Snapshot Ensembles aka Multi Checkpoint TTA - Training on small crops and finetune on big crops to speed up without loss in quality - Prediction equalization Slides: https://gh.mltrainings.ru/presentations/KibardinFattahovAbulkhanov_KaggleCamera_2018.pdf Github: https://github.com/ikibardin/kaggle-camera-model-identification Yandex hosts biweekly training sessions on machine learning. These meetings offer an opportunity for the participants of data analysis contests to meet, talk, and exchange experience. Each of these events is made up of a practical session and a report. The problems are taken from Kaggle and similar platforms. The reports are given by successful participants of recent contests, who share their strategies and talk about the techniques used by their competitors.
Views: 2000 ML Trainings
Talks@12: Data Science & Medicine
 
54:49
Innovations in ways to compile, assess and act on the ever-increasing quantities of health data are changing the practice and police of medicine. Statisticians Laura Hatfield and Sherri Rose will discuss recent methodological advances and the impact of big data on human health. Speakers: Laura Hatfield, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Sherri Rose, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Like Harvard Medical School on Facebook: https://goo.gl/4dwXyZ Follow on Twitter: https://goo.gl/GbrmQM Follow on Instagram: https://goo.gl/s1w4up Follow on LinkedIn: https://goo.gl/04vRgY Website: https://hms.harvard.edu/
NYC Open Data Meetup, R pacakge Caret workshop part1
 
52:04
NYC Open Data Meetup, R pacakge Caret workshop
Views: 827 Vivian Zhang
A Closer Look at KNN Solutions
 
02:00
This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
Views: 2480 Udacity
HOW TO BREAK A CAPTCHA | INTRODUCTION TO AI
 
26:14
GOVANIFY'S LAB #2 AI is always viewed as a huge black box, as something even creators don't understand and you are always shown the neuronal network part of the story, but what about showing to you the complete story about AI? As always in this series of video, through a silly project, I'm explaining to you how AI works and how state-of-the-art object detection works. I am also giving you resources to make you able to research on your own and learn more about the subject, by trying to make this as useful in passive and active learning once again! I've tried to improve as best as I could both the audio quality and video quality, even though the editing is still sort of lazy imho. I take all sort of criticisms and help requests about this, so feel free to contact me! I sincerely hope you liked this video! -G ~~~~~ Resources: TensorFlow: http://tensorflow.org/ OpenAI: https://openai.com Twitter: https://twitter.com/GovanifY Blog: https://govanify.com Mail: [email protected] (PGP available on the "About" page on my website) Video made using KdenLive and Audacity on Gentoo Linux Microphone: AT2035 XLR Audio Interface: Focusrite Scarlett 2i2 Camera: The Frankenstein phone(aka what happens when police breaks your phone and you repair the screen as you can, OnePlus 3t) Sources: Background Music: Hopeless Romantic, FearofDark 2-Minute Neuroscience - Medulla Oblongata, Neuroscientifically Challenged 3D Animation - Brain with Neurons Firing, TomsTwinFilms 4K Mask RCNN COCO Object detection and segmentation #2, Karol Majek Baxter the Robot playing Tic-tac-toe Game 4, Michael Overstreet But what _is_ a Neural Network _ Chapter 1, deep learning, 3Blue1Brown Cleverbot vs Humans, Humor Vault FUNNIEST CAPTCHA FAILS, Random GO 2015 SPL Finals - Nao-Team HTWK vs. B-Human 1st half, HTWK Robots Google's DeepMind AI Just Taught Itself To Walk, Tech Insider Hot Robot At SXSW Says She Wants To Destroy Humans _ The Pulse _ CNBC, CNBC How to use Tensorboard Embeddings Visualization with MNIST, Anuj shah MariFlow gets Gold in 50cc Mushroom Cup, SethBling Mind reading with brain scanners _ John-Dylan Haynes _ TEDxBerlin, TEDx Talks RASPUTIN - Vladimir Putin - Love The Way You Move (Funk Overload) @slocband, Pace Audioo Recurrent Neural Network Visualization, Michael Stone Walking around Shibuya - Tokyo - 渋谷を歩く- 4K Ultra HD, TokyoStreetView - Japan The Beautiful Tensorflow 14 Visualization Tensorboard 1 (neural network tutorials), 周莫烦 US Military Robot Dog will make a great companion for RoboCop, ArmedForcesUpdate Visualization of newly formed synapses with unprecedented resolution, Max Planck Florida Institute for Neuroscience I don't think I've used much of other sources, if you think you should be credited or do not like to be on this video please contact me Random thanks in no particular order: : Ely (sykhro), Frafnir, Batiste Flo., Xaddgx, Vlad
Views: 963 Gravof Corp
Rapidminer 5.0 Video Tutorial #4 - Genetic Optimization Part 1
 
08:40
In this video I highlight the data generation capabilities for Rapidminer 5.0 if you want to tinker around, and how to use a Genetic Optimization data pre-processor within a nested nested experiment. Yes, you read that correctly, a nested nested experiment.
Views: 22296 NeuralMarketTrends
8 Fun Machine Learning Projects for Beginners | Machine Learning with Python Online Training
 
41:45
Machine Learning with Python Master Program Training provided Online from USA industry expert trainers with real time project experience. 8 Fun Machine Learning Projects for Beginners | Machine Learning with Python Online Training - This is a video recording of a Live Webinar presentation by our Senior Data Science Expert and trainer. ==================================================== Get More Free Videos - Subscribe ➜ https://goo.gl/5ZqDML #zarantech #Machinelearningcourse #machinelearningwithpythontutorial #Machinelearningtrainingvideos COURSE PAGE: https://www.zarantech.com/machine-lea... REGISTER FOR FREE LIVE DEMO: https://promo.zarantech.com/free-webi... CONTACT: +1 (515) 309-7846 (or) Email - [email protected] "Machine Learning Projects" "Machine Learning with python tutorial" "free Machine Learning training" "online Machine Learning training" "Best Machine Learning training" "Machine Learning with python training for Beginners" "Best Machine Learning with python Training" "Python tutorials for beginners" "Machine Learning with python" ===================================================== Reviews / Testimonials from past trainees are saying: https://goo.gl/ZVfnE4 Refer your friends to ZaranTech - http://www.zarantech.com/be-a-friend-....
Views: 177 ZaranTech
RapidMiner 5 Tutorial - Video 10 - Feature Selection
 
03:23
Vancouver Data Blog http://vancouverdata.blogspot.com/
Views: 17985 el chief
Practical Machine Learning Tutorial with Python Intro p.1
 
05:55
The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are. In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we'll grab along the way. Machine learning was defined in 1959 by Arthur Samuel as the "field of study that gives computers the ability to learn without being explicitly programmed." This means imbuing knowledge to machines without hard-coding it. https://pythonprogramming.net/machine-learning-tutorial-python-introduction/ https://twitter.com/sentdex https://www.facebook.com/pythonprogra... https://plus.google.com/+sentdex
Views: 1688160 sentdex
Finale Doshi-Velez: "A Roadmap for the Rigorous Science of Interpretability" | Talks at Google
 
54:35
With a growing interest in interpretability, there is an increasing need to characterize what exactly we mean by it and how to sensibly compare the interpretability of different approaches. In this talk, I'll start by discussing some research in interpretable machine learning from our group, and then broaden it out to discuss what interpretability is and when it is needed. I'll argue that our current desire for "interpretability" is as vague as asking for "good predictions" -- a desire that. while entirely reasonable, must be formalized into concrete needs such as high average test performance (perhaps held-out likelihood is a good metric) or some kind of robust performance (perhaps sensitivity or specificity are more appropriate metrics). This objective of this talk is to start a conversation to do the same for interpretability: I will suggest a taxonomy for interpretable models and their evaluation, and also highlight important open questions about the science of interpretability in machine learning.
Views: 3148 Talks at Google
How to Successfully Harness AI to Combat Fraud and Abuse - RSA 2018
 
34:18
Slides and blog posts available at https://elie.net/ai This talk explains why artificial intelligence (AI) is the key to building anti-abuse defenses that keep up with user expectations and combat increasingly sophisticated attacks. It covers the top 10 anti-abuse specific challenges encountered while applying AI to abuse fighting, and how to overcome them. This video is a re-recording of the talk I gave at RSA 2018 on the subject
Views: 5435 Elie Bursztein
Computer Vision Video 2
 
00:34
TRACKS AND BOUNDING BOX: Tracks are logged for every object and are green when the object is outside the ROI and red when the object is inside the ROI. 'X' tracks represent an object that is walking, while 'O' tracks represent running objects. Bounding boxes are labeled according to object ID and colored in the following escalation: - Green: object outside ROI and walking - Yellow: object outside ROI and running - Orange: object inside ROI and walking - Red: object inside ROI and running KINEMATICS: Objects' motion is predicted using Kalman filters. Object velocity is calculated by x and y differencing of frame-to-frame centroids. Bounding box dimension changes are also calculated and logged. Both are filtered using a three-frame moving average. The ratio size to motion is used to determine if an object is running or walking. IDENTIFY EVENTS: The region of interest has been hard-coded at an off-the path area to show program capabilities. It is labeled with a red box. TIME IN REGION OF INTEREST: Objects are not flagged until they have been in the region of interest for 5 frames. They are un-flagged upon leaving. KEY FRAME SELECTION: Once flagged, a new video file is created following the naming conventions OBJECT_ID_FLAGGED.avi (with ID replaced with the object's ID). The area is recorded until 40 frames after the object is no longer flagged. ADDITIONAL FEATURES While motion is monitored and plotted, bound boxes are not created in the sky. When no objects are flagged, the processor saves time by running at 3x speed. It slows to normal speed once objects are flagged in the ROI, and resumes faster playback after object exits ROI. Running parameters and ratios are adjusted accordingly.
Views: 83 Ryan Sanders
Computational learning theory | Wikipedia audio article
 
03:56
This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Computational_learning_theory 00:00:16 1 Overview 00:03:32 2 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.7414932270060103 Voice name: en-GB-Wavenet-B "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms.
Views: 23 Subhajit Sahu
Computational learning theory
 
02:29
Please Subscribe our goal is 5000 subscriber for this year :) Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In supervised learning, an algorithm is given samples that are labeled in some useful way. For example, the samples might be descriptions of mushrooms, and the labels could be whether or not the mushrooms are edible. The algorithm takes these previously labeled samples and uses them to induce a classifier. This classifier is a function that assigns labels to samples including the samples that have never been previously seen by the algorithm Source:http://en.wikipedia.org/wiki/Computational_learning_theory
Views: 1006 Wikivoicemedia
Tech Talk: Teach JS Aesthetics with Machine Learning
 
51:24
Jonathan Martin will give you a whirlwind tour of the fundamental concepts and algorithms in machine learning, then explore a front-end application: selecting the "best" photos to feature on our photo sharing site. Don't expect mathematically laborious derivations of SVM kernels or the infinite VC dimension of Neural Nets, but we will gain enough intuition to make informed compromises (thanks to the No Free Lunch theorem, everything is a compromise) in our pursuit of aesthetically-intelligent machines. Find Jonathan on Twitter: @nybblr http://www.bignerdranch.com http://www.twitter.com/bignerdranch http://www.facebook.com/bignerdranch
Views: 85 Big Nerd Ranch
Harnessing Communities of Knowledge: Building an Automated Species Identification Tool
 
01:24:30
There exists communities of knowledge, and these communities are a distributed, social network of people. Some academic examples include the medical field, engineering, and the natural sciences; non-academic examples include stamp collectors, car enthusiasts and fashionistas. An important aspect of these communities is that the knowledge contained in the distributed network of people, as a whole, is greater than the sum of the individuals. Modern technology has introduced new components into these communities. The internet has made it faster, and easier to communicate, and the type of data that is communicated has become much richer, including images, videos, documents and code. We also have the ability to store and retrieve all of this data, so these communities are supporting both knowledge and vast quantities of data. In addition the world has become more connected, allowing more people to find and join communities, and start new ones. What, if anything, can we learn from these communities? Can we learn who knows what, and what their area of focus is? Can we learn how to combine information from multiple people within these communities? And can we distill the distributed knowledge of the community and make it centralized and consolidated so that anyone, anywhere can access it quickly and efficiently? I explore these questions through the naturalist community via the website iNaturalist. In this talk I will present models that learn the skills of the community members and are capable of combining those skills to predict the species label for an observation. I will discuss building computer vision datasets from data provided by this community, classification results on those datasets, and I will demo a new algorithm that reduces the memory requirement of large classification networks for fast on device inference. See more at https://www.microsoft.com/en-us/research/video/harnessing-communities-of-knowledge-building-an-automated-species-identification-tool/
Views: 700 Microsoft Research
NW-NLP 2018: Semantic Matching Against a Corpus
 
01:00:21
The fifth Pacific Northwest Regional Natural Language Processing Workshop will be held on Friday April 27, 2018, in Redmond, WA. We accepted abstracts and papers on all aspects of natural language text and speech processing, computational linguistics, and human language technologies. As with past four workshops, the goal of this one-day NW-NLP event is to provide a less-formal setting in the Pacific Northwest to present research ideas, make new acquaintances, and learn about the breadth of exciting work currently being pursued in North-West area. Morning Talks Title: Semantic Matching Against a Corpus: New Applications and Methods Speakers: Lucy Lin, Scott Miles and Noah Smith. Title: Synthetic and Natural Noise Both Break Neural Machine Translation Speakers: Yonatan Belinkov and Yonatan Bisk. Title: Syntactic Scaffolds for Semantic Structures Speakers: Swabha Swayamdipta, Sam Thomson, Kenton Lee, Luke Zettlemoyer, Chris Dyer and Noah A. Smith. See more at https://www.microsoft.com/en-us/research/video/nw-nlp-2018-semantic-matching-against-a-corpus-new-applications-and-methods-synthetic-and-natural-noise-both-break-neural-machine-translation-and-syntactic-scaffolds-for-semantic-structures/
Views: 1405 Microsoft Research