Bayesian Methods for Hackers An intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view. Prologue The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. If Bayesian inference is the destination, then mathematical analysis is a particular path towards it. Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. The choice of PyMC as the probabilistic programming language is two-fold. PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. Printed version now available! Bayesian Methods for Hackers is now available in print. Differences between the print version and the online version include: Contents Examples from the book
Tools The Social Media Research Foundation sustains the development of social media network analysis software. So far, it has supported the creation and dissemination of the NodeXL tool: NodeXL The Network Overview Discovery and Exploration Add-in for Excel (2007 / 2010 / 2013 / 2016) is an extension to the familiar Excel spreadsheet that helps collect, visualize and interpret social media networks. The Social Media Research Foundation is dedicated to making tools that help people understand social media and social networks. We produce NodeXL Basic which is available freely and openly to all. NodeXL Pro offers advanced features for importing social media data, calculating social network metrics, sentiment analysis, and publishing reports. NodeXL Pro is licensed to users on an annual basis: Registration keys will be required to run NodeXL Pro starting in October 2015! Contact info@smrfoundation.org for details! Your support keeps the NodeXL project active and strong, please upgrade to NodeXL Pro.
Apache Spark Apache Spark bugünlerde ismini daha sık duymaya başladığımız, büyük veri işleme amaçlı bir diğer proje. Hadoop’tan 100 kat daha hızlı olmak gibi bir iddia ile birlikte, gelişmiş “Directed Acyclic Graph” motoruna sahip, Scala dili ile yazılmış ve bellek-içi (in-memory) veri işleme özellikleriyle bu iddiayı boşa çıkartmıyor gibi görünüyor. Özellikle Yapay Öğrenme algoritmalarının dağıtık implementasyonu konusunda Hadoop’tan daha performanslı olduğunu söyleyebiliriz. Öyle ki, Apache Mahout projesi bundan böyle Hadoop ile değil Spark üzerinde çalışacak şekilde geliştirilmeye etme kararı aldı. Ancak şunu söylemeliyiz ki Spark Hadoop’un yerine geçecek bir teknoloji olmaktan ziyade, Hadoop ailesinin bir üyesi olup Hadoop’un zayıf kaldığı bazı konulardaki eksiklikleri giderecek gibi görünüyor. Logistic regression algoritmasının Hadoop ve Spark üzerinde çalıştırılması sonucu elde edilen performans örneklenmiş. Uygulama geliştirme açısından Spark Scala’nın avantajlarını sonuna kadar kullanıyor.
Google's Python Class | Python Education | Google Developers Welcome to Google's Python Class -- this is a free class for people with a little bit of programming experience who want to learn Python. The class includes written materials, lecture videos, and lots of code exercises to practice Python coding. These materials are used within Google to introduce Python to people who have just a little programming experience. The first exercises work on basic Python concepts like strings and lists, building up to the later exercises which are full programs dealing with text files, processes, and http connections. The class is geared for people who have a little bit of programming experience in some language, enough to know what a "variable" or "if statement" is. To get started, the Python sections are linked at the left -- Python Set Up to get Python installed on your machine, Python Introduction for an introduction to the language, and then Python Strings starts the coding material, leading to the first exercise.
Data science : apprendre la discipline en 8 étapes avec DataCamp Le métier de data scientist a été surnommé par Harvard Business Review comme « le plus sexy du 21e siècle » en 2012 et « le meilleur emploi de l’année » en 2016 par Glassdoor. Data Camp a dévoilé une infographie qui résume la façon d’apprendre la data science en 8 étapes. Un métier encore méconnu La position vis-à-vis de la data science a considérablement évolué au cours de ces quatre dernières années. Ils sont très importants, car il existe très peu de data scientists répondant aux attentes des entreprises à ce jour, bien que la définition de ce métier ne soit pas encore fixe. Avec plus de demande que d’offre, l’attention que l’on porte aux équipes de data scientists est à la hausse. De nombreuses compétences requises Tout comme la définition de data scientist, la définition de la data science a également de multiples facettes. De plus, des connaissances de SQL, des langages Python, Java et R sont généralement requises. Voici l’infographie réalisée par DataCamp :
Neural networks and deep learning The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. In each hemisphere of our brain, humans have a primary visual cortex, also known as V1, containing 140 million neurons, with tens of billions of connections between them. The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize digits like those above. Neural networks approach the problem in a different way. and then develop a system which can learn from those training examples. In this chapter we'll write a computer program implementing a neural network that learns to recognize handwritten digits. We're focusing on handwriting recognition because it's an excellent prototype problem for learning about neural networks in general. Perceptrons What is a neural network? So how do perceptrons work? That's the basic mathematical model.
Pegasus Data Project | Plate-forme d'expérimentation en humanités numériques, réseaux sociaux, Twitter, influence sur le web et visualisation de données Data Science Cheat Sheets – Python / R / MySQL & SQL / Spark / Hadoop & Hive / Machine Learning / Django – AITS – Data Mining Club Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms. There are thousands of packages and hundreds of functions out there in the Data science world! An aspiring data enthusiast need not know all. Here are the most important ones that have been brainstormed and captured in a compact few pages. Mastering Data science involves understanding of statistics, Mathematics, Programming knowledge especially in R, Python & SQL and then deploying a combination of all these to derive insights using the business understanding & a human instinct—that drives decisions. Here are the cheatsheets by category: Cheat sheets for Python: Python is a popular choice for beginners, yet still powerful enough to back some of the world’s most popular products and applications. Cheat sheets for R: The R’s ecosystem has been expanding so much that a lot of referencing is needed.
28 cайтов, на которых можно порешать задачи по программированию Не секрет, что лучший способ повысить свои навыки в программировании — это практиковаться и только практиковаться. Мы подготовили для вас огромную подборку сайтов с задачами по программированию на самые разные темы. Codeforces — несомненно самая популярная и известная платформа во всем мире для проведения соревнований на алгоритмику. Кроме крупных контестов сайт зачастую проводит свои «раунды» — участникам даются 5 задач на два часа. Есть система рейтинга, на основе которой участники делятся на два дивизиона. TopCoder — ненамного отстающая по популярности от Codeforces американская платформа. Timus Online Judge — русскоязычная (хотя английский язык также поддерживается) платформа, на которой более тысячи задач удачно отсортированы по темам и по сложности. SPOJ — крупный англоязычный сайт с более чем 20000 задачами на абсолютно разные темы: динамическое программирование, графы, структуры данных и т.д. CodinGame — сайт, на котором программирование и видеоигры сливаются в единое целое.
5 Big Data Use Cases To Watch - InformationWeek Here's how companies are turning big data into decision-making power on customers, security, and more. 10 Hadoop Hardware Leaders (Click image for larger view and slideshow.) We hear a lot about big data's ability to deliver usable insights -- but what does this mean exactly? It's often unclear how enterprises are using big-data technologies beyond proof-of-concept projects. Some of this might be a byproduct of corporate secrecy. Certainly the market for Hadoop and NoSQL software and services is growing rapidly. [Digital business demands are bringing marketing and IT departments even closer. According to Quentin Gallivan, CEO of big-data analytics provider Pentaho, the market is at a "tipping point" as big-data platforms move beyond the experimentation phase and begin doing real work. Here they are: 1. "That's all unstructured clickstream data," said Gallivan. A third big-source, social media sentiment, also is tossed into the mix, providing the desired 360 degree view of the customer. 2.