In a bold show of commitment, the two companies also announced they will be deploying each other’s cloud solutions internally. Through their unique partnership, the companies will co-engineer, go to market together with premier solutions and provide joint support services to ensure the best cloud experience for customers. SAP HANA Enterprise Cloud — SAP’s private managed cloud service — on Microsoft Azure will allow customers to run SAP S/4HANA in a secure, managed cloud. Additionally, Microsoft will deploy SAP S/4HANA on Azure to help run its own internal finance processes, and SAP will move its key internal business critical systems to Azure. Finally, SAP Ariba is currently utilizing Azure and is exploring further use within its procurement applications. Together, SAP and Microsoft will help companies make the most of running SAP applications in the cloud. “As technology transforms every business and every industry, organizations are looking for the right platforms and trusted partners to help accelerate their digital transformation,” said Satya Nadella, CEO of Microsoft. “Building on our longtime partnership, Microsoft and SAP are harnessing each other’s products to not only power our own organizations, but to empower our enterprise customers to run their most mission-critical applications and workloads with SAP S/4HANA on Azure.”
Visualization is one of the most exciting parts of data science. Plotting huge amounts of data to unveil underlying relationships has its own fun. Whether you’re identifying relationships between features or simply understanding the working of a model, visualizations are usually the best way to go about it. Visualizations also help explain your work to your customers and stakeholders. Python provides a lot of libraries, specifically for plotting and visualization and I usually have a tough time picking out which one to use for my problem statement. I recently come across with Altair, a visualization library in Python and I was amazed by its capabilities. It is a very user-friendly library which actually performs a lot of things with the minimal amount of code. Please note that Altair is still in development phase, so things might change over time. We can still do a lot of exciting work on it and the future potential really excites me – hence this article!
Natural language conversation is one of the most challenging artificial intelligence problems, which involves language understanding, reasoning, and the utilization of common sense knowledge. Previous works in this direction mainly focus on either rule-based or learning-based methods. These types of methods often rely on manual effort in designing rules or automatic training of model with a particular learning algorithm and a small amount of data, which makes it difficult to develop an extensible open domain conversation system. Chatbots (also known as Conversation Agents or Dialog based Agents) make use of Natural Language conversation models. They are the latest trend. All big guns investing a lot on building a Chatbot to allure customer because it adds a coolness factor to any system/ website/ application of being able to solve problems in more interactive way. Companies like Microsoft, Facebook (M), Apple (Siri), Google, WeChat, and Slack are heavily investing in building their version of bots Many companies are hoping to develop bots to have natural conversations that are as similar to human ones as possible, and many are claiming to be using NLP and Deep Learning techniques to make this possible. But with all the hype around AI it’s sometimes difficult to tell fact from fiction. In this article I will try to uncover what a chatbot consists of.
This demo presents the RNNoise project, showing how deep learning can be applied to noise suppression. The main idea is to combine classic signal processing with deep learning to create a real-time noise suppression algorithm that’s small and fast. No expensive GPUs required — it runs easily on a Raspberry Pi. The result is much simpler (easier to tune) and sounds better than traditional noise suppression systems (been there!).
In my previous tutorial Arima Models and Intervention Analysis we took advantage of the strucchange package to identify and date time series level shifts structural changes. Based on that, we were able to define ARIMA models with improved AIC metrics. Furthermore, the attentive analysis of the ACF/PACF plots highlighted the presence of seasonal patterns. In this tutorial we will investigate another flavour of intervention variable, the transient change.
Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.
We explore recurrent neural networks, starting with the basics, using a motivating weather modeling problem, and implement and train an RNN in TensorFlow.
8.5.0 has further improvements, including fully working sparse model support, empirically optimized exploration algorithms, a new cost-sensitive active learning algorithm (https://…/1703.01014 ), and baseline prediction support.
Predictive modeling is fun. With random forest, xgboost, lightgbm and other elastic models… Problems start when someone is asking how predictions are calculated. Well, some black boxes are hard to explain. And this is why we need good explainers.
Host RStudio server on an azure instance. Configure the instance to access RStudio with a nice url
We’re pleased to announce version 1.5.10 of RStudio Connect and the general availability of RStudio Connect Execution Servers. Execution Servers enable horizontal scaling and high availability for all the content you develop in R. The 1.5.10 release also includes important security improvements and bug fixes.