If you haven’t heard of Docker, it is a system that allows projects to be split into discrete units (i.e. containers) that each operate within their own virtual environment. Each container has a blueprint written in its Dockerfile that describes all of the operating parameters including operating system and package dependencies/requirements. Docker images are easily distributed and, because they are self-contained, will operate on any other system that has Docker installed, include servers. When multiple instances/users attempt to start a Shiny App at the same time, only a single R session is initiated on the serving machine. This is problematic. For example, if one user starts a process that takes 10 seconds to complete, all other users will need to wait until that process has completed before any other tasks can be processed.
IBM is leveraging Kubernetes to enable its Watson AI to run on public clouds AWS, Google, and Microsoft Azure. The move signals a shift in strategy for IBM.
I recently came across one of the most well-intended, and most unnerving, applications of AI in recruiting; a talking robot head pitched as a solution to avoid bias in interviewing. Picture a robot the size of an Alexa with an actual human face painted to the top. The face changes, tries to show expression and and non verbal cues. It wasn’t a joke either. The face was meant to be there. Think interviewing with Chucky if you need to visualize this.
I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them.
I have been working with Tensorflow during last months and I realized that, although there is a large number of Github repositories with many different and complex models, is hard to find a simple example that shows you how to obtain your own dataset from the web and apply some Deep Learning on it. In this post I pretended to provide an example of this task but being keeping it as simple as possible. I will show you how to obtain online unlabeled data, how to create a simple convolutional network, train it with some supervised data and use it later to classify the data we have gathered from the web.
I am going to give intuitive understanding of Regularization method in as simple words as possible. Firstly, I will discuss some basic ideas, so if you think you are already families with those, feel free to move ahead.
Have you heard of RStudio Connect, but do not know where to start? Maybe you are trying to show your manager how Shiny applications can be deployed in production, or convince a DevOps engineer that R can fit into her existing tooling. Perhaps you want to explore the functionality of RStudio’s Professional products to see if they fit the needs you have in your work. Today, we are excited to announce the RStudio QuickStart, which allows you to try out RStudio Connect for free from your desktop.
For the past year, my team and I have been working on a personalized user experience in the Taboola feed. We used Multi-Task Learning (MTL) to predict multiple Key Performance Indicators (KPIs) on the same set of input features, and implemented a Deep Learning (DL) model in TensorFlow to do so. Back when we started, MTL seemed way more complicated to us than it does now, so I wanted to share some of the lessons learned.
For background to this post, please see Learn #MachineLearning Coding Basics in a weekend. Here,we present the glossary that we use for the coding and the mindmap attached to these classes and upcoming book.
Feature engineering plays a key role in machine learning, data mining, and data analytics. This article provides a general definition for feature engineering, together with an overview of the major issues, approaches, and challenges of the field.
Emotion is a fundamental element of human society. If you think about it, everything worth analyzing is influenced by human behavior. Cyber attacks are highly impacted by disgruntled employees who may either ignore due diligence or engage in insider misuse. The stock market depends on the effect of the economic climate, which itself is dependent on the aggregate behavior of the masses. In the field of communication, it is common knowledge that what we say account for only 7% of the message while the rest 93% is encoded in facial expressions and other non-verbal cues. Entire fields of psychology and behavioral economics are dedicated to this field. That being said, the ability to measure and analyze emotions effectively will enable us to improve society in remarkable ways. For example, a psychology professor at the University of California, San Francisco, Paul Ekman, describes in his book, Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage, how reading facial expression can help psychologists find signs of potential suicide attempts while the patient lies about such intentions. Sounds like a job for facial recognition models? What about neural mapping? Can we effectively map emotional states from neural impulses? What about improving cognitive abilities? Or even emotional intelligence and effective communication? There are plenty of problems in the world to solve using the vast array of unstructured data that is available to us.
Dropout is commonly used to regularize deep neural networks; however, applying dropout on fully-connected layers and applying dropout on convolutional layers are fundamentally different operations. While it is known in the deep learning community that dropout has limited benefits when applied to convolutional layers, I wanted to show a simple mathematical example of why the two are different. To do so, I’ll define how dropout operates on fully-connected layers, define how dropout operates on convolutional layers and contrast the two operations.
Discussions on the interplay of humans and Artificial Intelligence tend to pose the issue in the language of opposition. However, according to the thinking of evolutionary biologist Richard Dawkins, tools such as AI can be better thought of as part of our extended phenotype. A phenotype refers to the observable characteristic of an organism, and the idea of the extended phenotype is that this should not be limited to biological processes, but include all of the effects that the genes have upon their environment, both internally and externally.
Deep Neural Networks are highly expressive machine learning networks that have been around for many decades. In 2012, with gains in computing power and improved tooling, a family of these machine learning models called ConvNets started achieving state of the art performance on visual recognition tasks. Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing.
Deep learning a subset of machine learning, has delivered super-human accuracy in a variety of practical uses in the past decade. From revolutionizing customer experience, machine translation, language recognition, autonomous vehicles, computer vision, text generation, speech understanding, and a multitude of other AI applications. In contrast to machine learning where an AI agent learns from data based on machine learning algorithms, deep learning is based on a neural network architecture which acts similarly to the human brain, and allows the AI agent to analyze data fed in?-?in a structure similar to the way humans do. Deep learning models do not require algorithms to specify what to do with the data, which is made possible thanks to the extraordinary amount of data we as humans, collect and consume?-?which in turn is fed to deep learning models .
Consensus is growing that model operationalization – rather than model development – is today’s biggest hurdle for data science. Production deployment techniques are generally one-offs, and data scientists and data engineers often lack the skills to operationalize models. Application integration, model monitoring and tuning, and workflow automation are often afterthoughts. Sometimes called the ‘last mile’ for analytics, this is where data science meets production IT. And it’s where business value is (or is not) created. Achieving the vision of becoming a model-driven business that deploys and iterates models at scale requires something that only a handful of companies have: ModelOps.