RStudio is excited to announce the availability of its flagship, enterprise-ready, integrated development environment for R in Azure Marketplace. RStudio Server Pro for Azure is an on-demand, commercially-licensed integrated development environment (IDE) for R on the Microsoft Azure Cloud. It offers all of the capabilities found in the popular RStudio open source IDE, plus turnkey convenience, enhanced security, the ability to manage multiple R versions and sessions, and more. It comes pre-configured with multiple versions of R, common systems libraries, and the most popular R packages. RStudio Server Pro Azure helps you adapt to your unique circumstances. It allows you to choose different Azure computing instances whenever a project requires it, and helps avoid the sometimes complicated processes for procuring on-premises software. If the enhanced security, elegant support for multiple R versions and multiple sessions, and commercially licensed and supported features of RStudio Server Pro appeal to you, consider RStudio Server Pro for Azure!
Rook is an open source cloud-native storage orchestrator for Kubernetes, providing the platform, framework, and support for a diverse set of storage solutions to natively integrate with cloud-native environments. Rook turns storage software into self-managing, self-scaling, and self-healing storage services. It does this by automating deployment, bootstrapping, configuration, provisioning, scaling, upgrading, migration, disaster recovery, monitoring, and resource management. Rook uses the facilities provided by the underlying cloud-native container management, scheduling and orchestration platform to perform its duties. Rook integrates deeply into cloud native environments leveraging extension points and providing a seamless experience for scheduling, lifecycle management, resource management, security, monitoring, and user experience.
This article requires basic knowledge of Linear Regression and is a pre-requisite for Logistic Regression in the final chapter of series. The term dependent and independent variable will be interchangeably used with a response and explanatory variable.
A Recommender System refers to a system that is capable of predicting the future preference of a set of items for a user, and recommend the top items. One key reason why we need a recommender system in modern society is that people have too much options to use from due to the prevalence of Internet. In the past, people used to shop in a physical store, in which the items available are limited. For instance, the number of movies that can be placed in a Blockbuster store depends on the size of that store. By contrast, nowadays, the Internet allows people to access abundant resources online. Netflix, for example, has an enormous collection of movies. Although the amount of available information increased, a new problem arose as people had a hard time selecting the items they actually want to see. This is where the recommender system comes in. This article will give you a brief introduction to two typical ways for building a recommender system, Collaborative Filtering and Singular Value Decomposition.
This time I won’t make any silly jokes or references’, that’s what SHE said! In today’s article, I’m gonna try to explain to you what I’ve been doing for the last week, and any comment or suggestion would be great, even if you feel that you are not concerned, maybe you are looking at it from an angle that could help, and you can find the the full code in this link ( make sure to choose the branch : ‘local-search-rl’ ). Anyway, in this part of the code, you can see that there’s the initial local search function and the q-local search one, it’s good to maintain the extensibility of the code, so when I need to implement a new algorithm, all I have to do is choose between them since they do not affect the other parts.
Check out this series of articles on Apache Spark. Each part is a 10 minute tutorial on a particular Apache Spark topic. Read on to get up to speed using Spark.
Developing intuitions through building and tuning a neural network using Keras in Google Colab
Step by step guide to run Machine Learning model in your own environment using Docker container
An Introduction to The Universal Open Standard Deep Learning Format and Using It In The Browser (ONNX/ONNX.js)
Data science is changing the rules of the game for decision making. Artificial intelligence is living its golden years where abundance of data, cheap computing capacity, and devoted talent depicts an unstoppable intelligence assisted life for humans. While it is common to hear about AI advice on health or financial investments, the same in business strategy is not so common. Maybe it is just a matter of time that AI learns how to handle data to support decision-making on business strategy, but it could also be that there is a lack of theoretical framework for it to build on. Following the competitive dynamics approach proposed in the article Strategizing with Competitive Asymmetry, a quantitative model was built to bridge this gap between business strategy and data science. In this article, I will outline an experiment that compares competitors’ data arranged in vectors using this framework. The outcome was an alternative similarity measure, Projection Similarity, as accurate as Cosine Similarity but with asymmetric similarity.
In this blog, we are going to build a neural network(multilayer perceptron) using TensorFlow and successfully train it to recognize digits in the image. Tensorflow is a very popular deep learning framework released by, and this notebook will guide for build a neural network with this library. If you want to understand what is a Multi-layer perceptron, you can look at my previous blog where I built a Multi-layer perceptron from scratch using Numpy.
This project contains an overview of recent trends in deep learning based natural language processing (NLP). It covers the theoretical descriptions and implementation details behind deep learning models, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning, used to solve various NLP tasks and applications. The overview also contains a summary of state of the art results for NLP tasks such as machine translation, question answering, and dialogue systems.
Imagine you are a company selling a fast-moving consumer good in the market. Let’s assume that the customer would follow the given journey to make the final purchase: Awareness -> Consideration -> Purchase. These are the states at which the customer would be at any point in the purchase journey. Now, how to find out in which state the customers would be after 6 months? Markov Chain comes to the rescue!!
Develop web crawlers with Scrapy, a powerful framework for extracting, processing and storing web data.