Zeit Online, a German newspaper, has created a series of data visualizations illustrating the emigration of millions of people from the eastern to western parts of Germany following the nation’s reunification in 1990. The visualizations show that nearly every former East German county lost more people than it gained to former West German areas between 1991-2017. This migration shrunk the populations of many former East German counties, reducing their tax revenues and resulting in effects that are still visible today, such as a lack of public infrastructure, including schools and hospitals.
The most significant new feature of RStudio Server Pro is Launcher, the long-awaited capability to separate the execution of R processes from the server where RStudio Server Pro is installed. Launcher allows you to run RStudio sessions and ad-hoc R scripts within your existing cluster workload managers, so you can leverage your current infrastructure instead of provisioning load balancer nodes manually. Now organizations that want to use Kubernetes or other job managers can run interactive sessions or batch jobs remotely and scale them independently.
RStudio is excited to announce RStudio Team, a new software bundle that makes it easier and more economical to adopt our commercially licensed and supported professional offerings. RStudio Team includes RStudio Server Pro, RStudio Connect, and RStudio Package Manager. With RStudio Team, your data science team will be properly equipped to analyze data at scale using R and Python; manage R packages; and create and share plots, Shiny apps, R Markdown documents, REST APIs (with plumber), and even Jupyter Notebooks, with your entire organization.
In this article, we had an overview of the Recommendation systems and how they provide an effective form of targeted marketing by creating a personalized shopping experience for each customer. However, we did not go deeper into the various methods of recommendations. This is because each of the methods is fairly extensive and deserve an article of its own. So in the next article, I shall discuss in detail as to how Recommendations Methods work and their advantages and disadvantages.
It is necessary to start by introducing the non-linear activation functions, which is an alternative to the best known sigmoid function. It is important to remember that many different conditions are important when evaluating the final performance of activation functions. It is necessary to draw attention to the importance of mathematics and the derivative process at this point. So, if you’re ready, let’s roll up the sleeves and get our hands dirty! ????
People hear a lot about how fantastic NLP is, but often for many it can be hard to see how it can be applied in a commercial setting. With that aim I thought I would share my first foray into using NLP to solve a business problem.
Artificial intelligence and machine learning are two of the most popular buzzwords in the market and many times are used interchangeably. They have become a part of everyday life, but that does not mean we understand them well. Lot of confusion exists between what is machine learning and what is AI. in most companies; marketing overlooks this distinction for advertising and sales. Let us go through some of the main differences between Artificial intelligence and machine learning in the following sections.
What is a Kernel Trick? In spite of its profound impact on the Machine Learning world, little is found that explains the fundamentals behind the Kernel Trick. Here we will take a look at it. By the end of this post, we will realize how simple the underlying concept is. And perhaps, this simplicity makes the Kernel Trick profound. If you’re reading this, you may already know as a fact that if there’s a dot product in a function we can use the Kernel trick. We typically come across this fact when learning about SVM.
1. Make business goal as clear as possible
2. Data have to be prepared
3. Define precisely the delivery
4. No black box effect
2. Data have to be prepared
3. Define precisely the delivery
4. No black box effect
Strategy and AI are terms that are hard to pin down – they mean different things to different people. Combine AI and Strategy – ???? ?? and now you have a harder problem to tackle! The goal of this post is to bring in the best advice out there about AI strategy and add a practitioner’s point of view for a successfully crafting AI strategy. This post is organized in three sections. Strategy and Planning, Building Blocks – Technology, People/Culture and Roadmap for launching and sustaining AI. In last couple of years it has become crystal clear that AI will be a major factor for business. Gartner included AI in its top 10 strategic technology trends for 2019. Most companies are keenly aware that they should have at least have an AI strategy. If nothing else, the propensity to follow AI rivals is high.
Deep learning and related machine learning advances have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed. In just the past two years, Google’s deep-learning-based AlphaGo defeated the world’s top Go players, surprising most AI experts who thought that it would take another 5 to 10 years to achieve such a milestone. Similarly, when Google switched to its new deep learning AI system in late 2016, it achieved an overnight improvement in the quality of its machine translations roughly equal to the total gains that the previous program had accrued over its 10 year lifetime. As is typically the case with major technology achievements, – e.g., the dot-com bubble, – deep learning has quickly climbed to the top of Gartner’s hype cycle, where all the excitement and publicity accompanying new, promising technologies often leads to inflated expectations, followed by disillusionment if the technology fails to deliver. AI may be particularly prone to such hype cycles, as the notion of machines achieving or surpassing human levels of intelligence leads to feelings of wonder as well as fear. Over the past several decades, AI has gone through a few such hype cycles, including the so-called AI winter in the 1980s that nearly killed the field.
What are the different branches of analytics?’ Most of us, when we’re starting out on our analytics journey, are taught that there are two types – descriptive analytics and predictive analytics. There’s actually a third branch which is often overlooked – prescriptive analytics. Prescriptive analytics is the most powerful branch among the three. Let me show you how with an example.
Google Cloud AutoML Vision simplifies the creation of custom vision models for image recognition use-cases. The concepts of neural architecture search and transfer learning are used under the hood to find the best network architecture and the optimal hyperparameter configuration that minimizes the loss function of the model. This article uses Google Cloud AutoML Vision to develop an end-to-end medical image classification model for Pneumonia Detection using Chest X-Ray Images.
WaveNet is a powerful new predictive technique that uses multiple Deep Learning (DL) strategies from Computer Vision (CV) and Audio Signal Processing models and applies them to longitudinal (time-series) data. It was created by researchers at London-based artificial intelligence firm DeepMind, and currently powers Google Assistant voices.
When I try to read about statistics I get mired in the jargon. Even just moving past the phrase, ‘For a given parameterized distribution,’ requires that I think about what it means for something to be ‘parameterized’ and what a ‘distribution’ is. I wind up reading in that plodding, word-by-word way that I might read a foreign language I happened to be studying. It’s exhausting. Here I’ve gathered notes from the lessons I’ve done so far in my data science boot camp to define the salient terms.
Applied Machine Learning is an empirical process where you need to try out different settings of hyperparameters and deduce which settings work best for your application. This technique is popularly known as Hyperparameter Tuning. These hyperparameters could be the learning rate(alpha), number of iterations, mini batch size, etc.