This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, ouliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC. Previous entries are listed below the picture.

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As we all push forward with the development of artificial intelligence (AI) solutions, software developers and data scientists increasingly want to leverage deep learning frameworks. To back up a bit, deep learning is a type of machine learning that can enable more complex solutions based on evaluation of abstractions of data. Scaling through added layers and processing, deep learning can build in aggregate from user input and experiences, much the way people learn. Deep learning frameworks enable algorithms to continually improve their performance on complex tasks like speech and image recognition. To get on this path to a new generation of AI solutions, developers and data scientists need to find ways to reduce the steep learning curve that comes with the deployment and configuration of deep learning frameworks. Then, find ways to accelerate the development, training, and deployment of models.

This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.

Google Assistant is having a great month. Just last week Google started rolling out its voice-activated aide to all Android phones running Marshmallow and Nougat, and now it's showing off a new trick that will make it much more useful: integration with our messages. For whatever reason, Google Assistant struggled when it came to reading our messages before. Even though it had a second home in Allo and could send messages on command, Assistant balked when asked basic questions about our incoming texts, as it was unable to read and interact with them. As first spotted by 9to5Google, that's changed in the latest update. Now you can ask Assistant to hear the last message you received, dictate the one you just sent, and even read the most recent message from a specific person.

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This is a single day course from 9:00am to 2:00pm. You will need to bring your laptop and have python, TensoFflow 1.0 and pandas installed before the class. You can find the instructions here. If you have any difficulties let us know before the day of the training and we will provide you with support. We will be running two parallel sessions, one for new users who have minimal or no experience with TensorFlow and another one for advanced users.

It seems like everyone wants to invest in artificial intelligence (AI). And it's not just the tech giants: USAA is using AI to protect its users from identity theft and Under Armour has connected its health app, MyFitnessPal, to IBM Watson so users can get a more thorough read of their health. AI is already a $15 billion dollar industry, according to the MIT Technology Review, with more than 2,600 companies developing their own tech, and the value of AI is reported to rise to over $70 billion by 2020. Because of AI's business opportunities, hundreds of designers in digital agencies, people who were taught to create products and services that live on the Internet, are starting to build physical products that interact with us, respond to our moods, and make decisions for us. It's a challenge that requires every skill they've learned, plus many they haven't. Still, designers know the basics: The principles of user-centric design lay the groundwork for building a great AI system.

This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyone who wants to model sequential data of some sort. The tutorial assumes that the reader knows the basics of math and programming, but does not assume any particular experience with neural networks or natural language processing. It attempts to explain the intuition behind the various methods covered, then delves into them with enough mathematical detail to understand them concretely, and culiminates with a suggestion for an implementation exercise, where readers can test that they understood the content in practice.

If you've ever wondered how two or more things relate to each other, or if you've ever had your boss ask you to create a forecast or analyze relationships between variables, then learning regression would be worth your time. In this article, you'll learn the basics of simple linear regression - a tool commonly used in forecasting and financial analysis. We will begin by learning the core principles of regression, first learning about covariance and correlation, and then moving on to building and interpreting a regression output. A lot of software such as Microsoft Excel can do all the regression calculations and outputs for you, but it is still important to learn the underlying mechanics. At the center of regression is the relationship between two variables called the dependent and independent variables.