Kaggle is a community and site for hosting machine learning competitions. Competitive machine learning can be a great way to develop and practice your skills, as well as demonstrate your capabilities. In this post, you will discover a simple 4-step process to get started and get good at competitive machine learning on Kaggle. How to Get Started with Kaggle Photo by David Mulder, some rights reserved. I took my last response to this question and decided to turn it into this blog post.I hope you find it useful.

Webpack is the premier build tool for React and Angular 2 applications. In this course you;ll master each major feature of Webpack and learn how to optimize it for your own app. Webpack has a wealth of fancy features, but each requires in depth knowledge of how they work.

The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? What is the best order in which to use selected resources? It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won't be necessary; some extra time spent on the earlier steps should help compensate.

The Microsoft Azure ML team recently announced the availability of 3 ML templates on the Azure ML Studio – for online fraud detection, retail forecasting and text classification. These templates demonstrate industry best practices and common building blocks used in an ML solution for a specific domain, starting from data preparation, data processing, feature engineering, model training to model deployment (as a web service) . The goal for Azure ML templates is to make data scientists more productive and faster in building and deploying their custom ML solutions on the cloud. Templates include a collection of pre-configured Azure ML modules as well as custom R scripts in the Execute R Script modules to enable an end-to-end solution. We'll walk through these templates in detail in this and future webinars.

Anyone can teach themselves to have a memory the size of a champion, a study shows. Scans found ordinary members of the public had brains as sharp as the world's greatest memorisers after a simple brain training course using'memory palaces'. It means the ability to perform astonishing feats - such as remembering lists of several dozen words - can be learned, say scientists. After 40 days of daily 30-minute training sessions individuals who had typical memory skills at the start and no previous practise more than doubled their capacity. In this study, the learning strategy scientists chose was loci training, also known as creating a'memory palace'.

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 RuS-SIR 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.