Review of the most interesting materials on data analysis and machine learning №36 (February 16 - 22, 2015)

I present to you the next issue of the review of the most interesting materials on the topic of data analysis and machine learning.

General

• Seventh annual Microsoft Research Summer School. This time about machine learning and intelligence
• IBM Watson for Oncology: helping the cognitive system to fight cancer
• Artificial intelligence that can understand what the video is about.
• Russian-language resources on statistics, machine learning, R
• Infographics: the history of Data Science
• Microsoft strengthens the position of Python and Linux in their new tools in the field of Big Data
• The beginning of the path and development in the field of machine learning is a good motivating article from the author of the blog MachineLearningMastery.
• Currently, Apache Spark looks like the future of Big Data.
• Deeplearning4j: Deep Learning Library for Java
• Data Science: list of active blogs
• Microsoft announced the general availability of the platform Azure Machine Learning
• How Pinterest Fights Spam
• Google made the MapReduce library available using C / C ++
• Pintereset: Real-Time Analytics - Pinterest's engineers review their real-time analytics systems architecture.
• What to do during model training runs - some interesting ideas from the author of the blog MachineLearningMastery about what to do during pauses that may arise during the launch of the model training processes in machine learning tasks.
• Channel 9 has developed an API recommendation for Azure ML

Theory and algorithms of machine learning, code examples

• Machine learning - 1. Correlation and regression. Example: site visitors conversion
  
• Apache Spark Introduction
• Review of audio analysis algorithms
• Billiard bot: the history of creation
• Neural networks in Azure ML - introduction to Net #
• Introduction to Bayesian networks using R
• Basics of paralleling using R programming language
• Getting data from Arduino sensors using R programming language
• Kayak: library for working with deep neural networks
• Example of hierarchical clustering - a visualized example of hierarchical clustering, created using the R programming language and the Shiny visualization library.
• A series of lessons on machine learning and natural language processing. Lesson 3: Bayes theorem

Machine learning competitions

• Machine Learning Competition: Microsoft Malware Classification Challenge (BIG 2015)
• Machine Learning Competition: March Machine Learning Mania 2015

Online courses, educational materials and literature

• Introduction to the course "Image and video analysis". Lectures from Yandex
• Online course: D003x.1: Applications of Linear Algebra Part 1
• Online course "Model Building and Validation - Advanced Techniques for Analyzing Data"
• Book Review: Data Mining for Managers
• Interview with the author of the book "R Machine Learning Essentials"

Video, podcasts

• Apache Spark - SDK for all Big Data platforms - an interesting report on Apache Spark. In this speech, Pat McDonough talks about the development of Apache Spark and the possibility of using this product in the field of data processing and analysis.
• DataFrame in Apache Spark for scaling Data Science tasks - video from a recent mitap in addition to the news that Apache Spark 1.3 will have a new opportunity to use DataFrame. Actually in this video, Reynold Xin will talk about this new functionality in Apache Spark.
• Introducing Deep Learning Using Python

Data engineering

• Microsoft makes Apache Storm publicly available in its Azure cloud platform
• Recent Apache Spark - Reynold Xin Performance Improvements provides an overview of recent significant Apache Spark performance improvements.
• Announcement of DataFrame in Apache Spark - Apache Spark version 1.3 will be able to use DataFrame, this article will tell you about the details of implementing and using DataFrame in Apache Spark.
• Extending analytics in MemSQL with Apache Spark

Reviews

• Interesting from the world of R (February 9-15, 2015)
• The best materials for the week from KDnuggets.com (February 8 - 14)
• Data Science News from MyDataMine.com (February 21)
• Big Data News from MyDataMine.com (February 18)
• Weekly Digest from DataScienceCentral (February 23)
• Weekly Digest from DataScienceCentral (February 16)
• The best resources for the week from Data Elixir (№23)
• Weekly collection of the best materials from R1Soft (February 20)
• The most interesting materials on High Scalability (February 20)

Previous release: Review of the most interesting materials on data analysis and machine learning №35 (February 9 - 15, 2015)