2017 Jan Tutorials, Overviews

• Deep Learning Research Review: Natural Language Processing - Jan 31, 2017.
  This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.

  Deep Learning, Natural Language Processing, Neural Networks, NLP

• Internet of Things Tutorial: IoT Devices and the Semantic Sensor Web - Jan 30, 2017.
  IoT applications have to collect and analyze information from multiple heterogeneous objects. Dealing with multiple sensors and internet connected objects, at multiple levels, requires attention. Read on to find out more.

  Internet of Things, IoT, Sensors

• Pandas Cheat Sheet: Data Science and Data Wrangling in Python - Jan 27, 2017.
  The Pandas library can seem very elaborate and it might be hard to find a single point of entry to the material: with other learning materials focusing on different aspects of this library, you can definitely use a reference sheet to help you get the hang of it.

  Cheat Sheet, Data Preparation, DataCamp, Pandas, Python

• Artificial Intelligence and Speech Recognition for Chatbots: A Primer - Jan 26, 2017.
  Bot bots bots... Read this overview of how artificial intelligence and natural language processing are contributing to chatbot development, and where it all goes from here.

  AI, Artificial Intelligence, Chatbot, Speech Recognition

• Why the Data Scientist and Data Engineer Need to Understand Virtualization in the Cloud - Jan 25, 2017.
  This article covers the value of understanding the virtualization constructs for the data scientist and data engineer as they deploy their analysis onto all kinds of cloud platforms. Virtualization is a key enabling layer of software for these data workers to be aware of and to achieve optimal results from.

  Cloud, Data Engineer, Data Engineering, Data Science, Data Scientist, Virtualization

• Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms - Jan 25, 2017.
  Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.

  Algorithms, Machine Learning, Programming, Python

• Creating Curious Machines: Building Information-seeking Agents - Jan 24, 2017.
  Researchers at Maluuba are developing ways to teach artificial agents how to seek information actively, by asking questions. This includes a deep neural agent that learns to accomplish these tasks through efficient information-seeking behaviour, a vital step towards Artificial General Intelligence.

  Agents, AI, Deep Learning, Machine Intelligence, Maluuba, Neural Networks

• The Top Predictive Analytics Pitfalls to Avoid - Jan 23, 2017.
  Predictive modelling and machine learning are significantly contributing to business, but they can be very sensitive to data and changes in it, which makes it very important to use proper techniques and avoid pitfalls in building data science models.

  Bias, Machine Learning, Model Performance, Predictive Analytics, Regularization, Statistics

• Chatbots on Steroids: 10 Key Machine Learning Capabilities to Fuel Your Chatbot - Jan 23, 2017.
  As chatbots become a common practice, the need for smarter bots arises. Empowering your bot with machine learning capabilities can really differentiate it from the rest. Check out these 10 capabilities to help fuel your chatbot.

  AI, Chatbot, Machine Learning, Sentiment Analysis

• Going to War with the Giants: Automated Machine Learning with MLJAR - Jan 19, 2017.
  The performance of automated machine learning tool MLJAR on Kaggle competition data is presented in comparison with those from other predictive APIs from Amazon, Google, PredicSis and BigML.

  Amazon, Automated Data Science, Automated Machine Learning, BigML, Google, Kaggle, XGBoost

• The big data ecosystem for science: X-ray crystallography - Jan 19, 2017.
  Diffract-and-destroy experiments to accurately determine three-dimensional structures of nano-scale systems can produce 150 TB of data per sample. We review how such Big Data is processed.

  Big Data, Science, Strata, X-ray crystallography

• The Current State of Automated Machine Learning - Jan 18, 2017.
  What is automated machine learning (AutoML)? Why do we need it? What are some of the AutoML tools that are available? What does its future hold? Read this article for answers to these and other AutoML questions.

  Automated, Automated Data Science, Automated Machine Learning, Hyperparameter, Machine Learning

• Time Series Analysis: A Primer - Jan 17, 2017.
  Time series analysis is a complex subject but, in short, when we use our usual cross-sectional techniques such as regression on time series data, variables can appear "more significant" than they really are and we are not taking advantage of the information the serial correlation in the data provides.

  Data Analysis, Time Series

• 90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning (updated) - Jan 17, 2017.
  Stay up-to-date in the data science with active blogs. This is a list of 90 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  Big Data, Blogs, Data Mining, Data Science, Machine Learning

• Introduction to Forecasting with ARIMA in R - Jan 16, 2017.
  ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining time series for demand at a bike-sharing service, fitting an ARIMA model, and creating a basic forecast.

  ARIMA, Datascience.com, Forecasting, R, Stationarity, Time Series

• A Concise Overview of Recent Advances in Chatbot Technologies - Jan 13, 2017.
  2016 saw some big leaps in chatbot technologies (along with a few unforeseen embarrassments). Get a quick review of the big events in the space over the past year, complete with supporting videos.

  Bots, Chatbot, GRAKN.AI

• A Concise Overview of Recent Advances in the Internet of Things (IoT) - Jan 12, 2017.
  A lot happened in IoT during 2016. Read this post for a briefing on some of the most important events, how they unfolded, and what they mean moving forward, complete with select videos to reinforce and elaborate.

  GRAKN.AI, Internet of Things, IoT

• A Concise Overview of Recent Advances in Vehicle Technologies - Jan 11, 2017.
  2016 was a big year for electric and driverless cars. Get a quick review with relevant videos on some of the events of interest in the field during the past year.

  China, Google, GRAKN.AI, Self-Driving Car, Tesla, Vehicle Analytics

• Internet of Things Tutorial: WSN and RFID – The Forerunners - Jan 6, 2017.
  WSN and RFID are key to understanding more complex IoT concepts and technologies, but also the structure of non-trivial IoT systems, which are very likely to comprise RFID or WSN components.

  Internet of Things, IoT, RFID, Sensors

• Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall - Jan 5, 2017.
  Data science and predictive analytics can provide huge value, but they can mislead and backfire if not used with fail-safe measures. The author gives examples of such problems and provides guidelines to avoid them.

  Advice, Data Science, Model Performance, Overfitting, Predictive Analytics, Statistical Modeling

• Creating Data Visualization in Matplotlib - Jan 5, 2017.
  Matplotlib is the most widely used data visualization library for Python; it's very powerful, but with a steep learning curve. This overview covers a selection of plots useful for a wide range of data analysis problems and discusses how to best deploy each one so you can tell your data story.

  Data Visualization, Datascience.com, Matplotlib, Python

• Tidying Data in Python - Jan 4, 2017.
  This post summarizes some tidying examples Hadley Wickham used in his 2014 paper on Tidy Data in R, but will demonstrate how to do so using the Python pandas library.

  Data Cleaning, Data Preparation, Pandas, Python

• Generative Adversarial Networks – Hot Topic in Machine Learning - Jan 3, 2017.
  What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.

  Deep Learning, Generative Adversarial Network, Machine Learning, Neural Networks, NIPS

• 3 methods to deal with outliers, by Alberto Quesada - Jan 3, 2017.
  In both statistics and machine learning, outlier detection is important for building an accurate model to get good results. Here three methods are discussed to detect outliers or anomalous data instances.

  Machine Learning, Outliers, Statistics

• Machine Learning and Cyber Security Resources - Jan 2, 2017.
  An overview of useful resources about applications of machine learning and data mining in cyber security, including important websites, papers, books, tutorials, courses, and more.
   
   

  Cybersecurity, Machine Learning, Security