Suluk Kanna Nabi

Dalam sebuah riwaya dikisahkan bahwa di waktu Rasulullah S.A.W sedang bartawah di Ka’bah beliau mendengar seorang arab badui di hadapannya bertawaf sambil berdzikir Ya Karim…. Ya Karim….. Rasulullah S.A.W menirunya membaca Ya Karim… Ya Karim…., arab badui itu lalu berhenti di salah satu sudut Ka’bah dan berdzikir lagi Ya Karim… Ya Karim…. Rasulullah S.A.W yang berada di belakangnya mengikuti dzikirnya Ya Karim… Ya Karim…. Merasa seperti diolok – olok, arab badui itu menoleh ke belakang dan terlihat olehnya seorang laki – laki yang gagah lagi tampan yang belum pernah di kenalinya., arab badui itu pun berkata : “Wahaiorang tampan! Apakah engkau sengaka mengolok – olokku, karena aku ini adalah orang arab badui? kalaulah bukan karena ketampananmu dan kegagahanmu pasti engkau akan aku laporkan kepada kekasihku Muhammad Rasulullah S.A.W.” Mendengar kata – kata orang arab badui itu Rasulullah S.A.W pun tersenyum, lalu bertanya: “Tidakkah engkau mengenali Nabimu wahai orang arab ? Arab badui pun menjawab : “Belum” Rasulullah bertanya kembali kepadanya: “Bagaimana engkau beriman kepadanya ?” Arab badui itu menjawabnya: “Demi Allah…. aku percaya dengan sangat yakin atas kenabianya, sekalipun aku belum pernah melihatnya, dan aku membenarkan kenabianya sekalipun aku belum pernah berjumpa denganya” Rasulullah S.A.W pun berkata kepadanya: “Wahai orang arab ketahuilah…....Read more …

zulaibib

Di sudut kota madinah tinggalah seorang pemuda bernama zulaibi. dikenal sebagai pemuda yang baik di kalangan para sahabat. Juga dalam hal ibadahnya termasuk orang yang rajin dan taat, dari sudut ekonomi dan finansial, ia put tergolong orang yang tidak punya. Sebagai seorang yang telah dianggap mampu, ia hendak melaksanakan sunnah rosul yaitu menikah. Beberapa kali ia meminang gadis di kota itu, namun selalu di tolah oleh pihak orang tua ataupun sang gadis dengan berbagai alasan. Zulaibib kemudian mengutarakan isi hatinya kepada baginda Nabi. Sambi tersenyum beliau berkata : “Maukah engkau saya nikahkan dengan putri dari kalangan Ansyar?”. Zulaibib pun menjawab ” saya belum berani ya rasul, putri sahabat itu terkenal akan kecantikan dan kesholihahanya dan hingga kini ayahnya selalu menolak lamaran dari siapapun”. Dan hari beikutnya ketika bertemu dengan zulaibib rosulullah menanyakan hal yang sama.”zulaibib, tidakkah engkau menikah ?”. Dan zulaibib menjawab dengan jawaban yang sama. Begitu, begitu dan begitu. Tiga kali dalam tiga hari berturut – turut. Dan akhirnya hari ketiga itulah rosulullah menarik lengan zulaibib dan membawanya ke salah satu rumah seorang pemimpin Asnhor. Rosulullah berkata “Aku ingin menikahkan putri kalian”. Dan dijawab oleh si tuan rumah ” betapa indahnya dan betapa barokahnya rumah kita…. oh… ya rasulullah,...Read more …

Machine Learning as a Service – MLaaS

datasciencecentral.com MLaaS is neither new nor rocket science or an unknown service. In today’s time there are hundreds of companies in this domain which are working as a service provider of MLaaS (SPMLaaS). Machine learning is into so many services and applications as on date and we may not even aware of them or most of them. In the area of FinTech, Medical, Law and almost every service which needs/has repeated actions/steps every time has made use of it as a service knowingly or unknowingly. Feature engineering as an essential to applied machine learning. Using domain knowledge to strengthen your predictive model or prescriptive model out of prediction can be both difficult and expensive. To help fill the information gap on feature engineering, MLaaS hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena. When I came across the assertion that each bootstrap sample always contain on average approximately 2//3 of the observations. I did not know the secrete and never understood that the chance of not being selected in any of draws (say n) from samples (say n) with replacement as  . I never know that each bootstrap sample or bagged tree will contain on average...Read more …

Validating Machine Learning Detection of Mobile Malware

blog.zimperium.com Zimperium’s core machine learning engine, z9, has a proven track record of detecting zero-day exploits. We recently announced an extension of the framework that detects previously unknown mobile malware. This extension is known as “z9 for Mobile Malware”, and was officially announced in September 2017. Internally, the code name has been “Cogito”, so this research blog will use that name throughout. On a pool of approximately 1800 samples collected from the Play Store1, Cogito detected two of them as malicious in a matter of seconds. This post outlines the process our team took to validate Cogito’s behavioral detection of the two malicious apps. Checking the behavioural information extracted by Cogito we noticed that those samples are really aggressive on Ad displaying. In fact, fullscreen Ads are displayed each time: An application is installed, updated or uninstalled; A flag on Accessibility Services is triggered; The screen is unlocked; The user navigates from a page to another of the application. One of the two applications also contained really suspicious code to auto-click Ads issued by Facebook. Applications metadata The updated application information was the following (right before being removed by Google): Application Name: Phone Cleaner Dev Package Name: com.life.read.physical.trian Play Store...Read more …

Free Deep Learning Book (MIT Press)

datasciencecentral.com The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. Source for picture: click here Content Table of Contents Acknowledgements Notation 1 Introduction Part I: Applied Math and Machine Learning Basics 2 Linear Algebra 3 Probability and Information Theory 4 Numerical Computation 5 Machine Learning Basics Part II: Modern Practical Deep Networks 6 Deep Feedforward Networks 7 Regularization for Deep Learning 8 Optimization for Training Deep Models 9 Convolutional Networks 10 Sequence Modeling: Recurrent and Recursive Nets 11 Practical Methodology 12 Applications Part III: Deep Learning Research 13 Linear Factor Models 14 Autoencoders 15 Representation Learning 16 Structured Probabilistic Models for Deep Learning 17 Monte Carlo Methods 18 Confronting the Partition Function 19 Approximate Inference 20 Deep Generative Models Bibliography Index source: https://www.datasciencecentral.com/profiles/blogs/free-deep-learning-book-mit-pressRead more …

Can Machine Learning Outsmart Malware?

darkreading.com Using machine learning in the cybersecurity domain is a growing trend with many advantages, but it also has its risks. Fighting malware is a modern arms race. Not only has malware evolved to be more evasive and harder to detect, but their vast numbers make it even more difficult to handle. As a result, detecting a malware has become a big data problem which requires the help of self-learning machines to scale the knowledge of analysts, handle the complexity beyond human capabilities, and improve the accuracy of threat detection. There are number of approaches to this problem; choosing the right algorithm to serve the security engine’s purpose is not an easy task. In this article, we will refer to machine learning (ML) as an application of artificial intelligence (AI) where computers learn without being explicitly programmed. We will look into some use cases and challenges, starting with an interesting question: why do we see this growing trend now? The answer has to do with lower costs and increased availability of private and public cloud technology for collecting, storing and analyzing big data in real time, and the academic research progress in ML and related algorithms such as Deep Neural Networks...Read more …

14 Great Articles and Tutorials on Clustering

www.datasciencecentral.com. This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC. 14 Great Articles and Tutorials on Clustering Fast clustering algorithms for massive datasets Clustering idea for very large datasets Spectral Clustering – How Math is Redefining Decision Making Variance, Clustering, and Density Estimation Revisited K Means Clustering – Effect of random seed Crazy Data Science Tutorial: Classification and Clustering Newbie trying to cluster mixed data type variables in SAS Feature engineering for building clustering models Ward’s Method for clustering in SAS Clustering responses to define dependent variable for logistic regr… Clustering with non numeric data Find Marketing Clusters in 20 minutes in R Jackknife logistic and linear regression for clustering and predict… Cluster analysis Enjoy the reading, or become one of our bloggers and start posting articles and tutorials on DSC. source : https://www.datasciencecentral.com/profiles/blogs/14-great-articles-and-tutorials-on-clusteringRead more …

A second set of eyes – Using computers to aid melanoma detection

IBM.COM The deadliest skin cancer is melanoma, which will be responsible for over 9,000 deaths in the United States in 20171. Melanoma is unique among cancers in that it arises as a visible and identifiable mark on the surface of the skin – unlike cancers of the breast, lung, or colon that develop hidden from our view. This would suggest that computer vision, which has demonstrated human equivalency in visual recognition tasks such as facial and object identification, would be ideally suited to aid in early detection of melanoma. However, physicians and patients continue to rely upon their naked eye to recognize melanoma. This begs an obvious question: why aren’t computers aiding the human eye in melanoma detection? Figure 1 – An example of a melanoma skin lesion (left) and a benign mole (right)             The reason, in my opinion, is not due to a deficiency in computer vision technology or an innate complexity of melanoma detection. Rather, the biggest roadblock to date has been the inability of the medical community to generate large, well-designed, public datasets of skin images with requisite metadata to train systems for accurate detection. This dataset bottleneck has prohibited the...Read more …