Tag: data scientist

Feed: All - O'Reilly Media. Author: Ben Lorica. Stockton Infinity Bridge (source: darren price on Flickr) There will back-to-back half-day tutorials on reinforcement learning at the upcoming Artificial Intelligence Conference in San Francisco: Building reinforcement learning applications with Ray and Introduction to reinforcement learning. As more companies begin to experiment with and deploy machine learning in different settings, it’s good to look ahead at what future systems might look like. Today, the typical sequence is to gather data, learn some underlying structure, and deploy an algorithm that systematically captures what you’ve learned. Gathering, preparing, and enriching the right data—particularly training ... Read More

Feed: All - O'Reilly Media. Author: Courtney Allen. TV genome visualization (source: bluefin on Wikimedia Commons) Jay Jacobs will teach a two-day tutorial course on data analysis and machine learning for cybersecurity at the O'Reilly Security Conference, October 29 to November 1, 2017, in New York City. Early price ends September 14. Subscribe to the O'Reilly Security Podcast to examine the challenges and opportunities for security practitioners, with a focus on the people on the frontlines of security, working to build better defenses. Find us on Stitcher, iTunes, SoundCloud, RSS. In this episode of the Security Podcast, Courtney Nash, former ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Jesus Ramos. Data Denialism A common scenario that data analysts in general encounter is what I like to describe as "data denialism". Often, and especially while consulting, an analyst will find that the data tells a different story than what the customer holds to be true. It is also often the case that, when presenting this finding, the customer will outright deny the evidence, asserting that either the data or the analysis must be wrong. For example, it may be that a retailer focused on the low-end market is getting most ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. The intelligence in AI is computational intelligence, and a better word could be Automated Intelligence. But when it comes to good judgment, AI is not smarter than the human brain that designed it. Many automated systems perform poorly, to the point that you are wondering if AI is an abbreviation for Artificial Innumeracy,  Critical systems - automated piloting, running a power plant - usually do well with AI and automation, as considerable testing is done before deploying these systems. But for many mundane tasks, such as spam detection, chatbots, ... Read More

Feed: Hortonworks Blog – Hortonworks. Author: Syed Mahmood. Data science is inherently an exploratory and creative process because there is usually neither a definitive answer to the problem at hand nor a well-defined approach to reaching one. Data scientists research problems, explore data, visualize patterns across data and use their experience and judgment to choose parameters and processes that may be relevant to the specific problem at hand. This makes sharing and collaboration a critical activity that enables teams of data scientists to build on each other’s knowledge and to produce the overall best results. As data science has evolved ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Luba Belokon. Generative adversarial networks (GANs) are a class of neural networks that are used in unsupervised machine learning. They help to solve such tasks as image generation from descriptions, getting high resolution images from low resolution ones, predicting which drug could treat a certain disease, retrieving images that contain a given pattern, etc. Our team asked a data scientist, Anton Karazeev, to make the introduction to GANs engine and their applications in everyday life. GANs were  introduced by Ian Goodfellow in 2014.They aren’t the only approach of neural networks in ... Read More

Feed: Hortonworks Blog – Hortonworks. Author: Casey Stella. The Motivation Many cybersecurity problems are also big data problems.   What is more and more apparent, though, is that these problems are also problems solved by data science.  The modern cybersecurity practitioner solves cybersecurity problems by making sense of data.  This is firmly within the sweet spot where data science meets big data and reaps rewards.  The Metron team at Hortonworks is keenly aware of this relationship as we are building a cybersecurity platform atop big data infrastructure.  While there are many great platforms to use for dealing with the volume and ... Read More

Feed: Latest imported feed items on Analytics Matters. “Lessons from the Sentient Enterprise” is a series of posts timed around the publication of The Sentient Enterprise, a new book on big data analytics by Oliver Ratzesberger and Mohan Sawhney. Each post in the series highlights a major theme covered in book and at executive workshops being held in conjunction with its upcoming release by Wiley Publishers. Judging by the number of publisher meetings and galley review sessions lately, Mohan Sawhney and I won’t have to wait much longer for our new book, The Sentient Enterprise, to hit the streets. Some ... Read More

Feed: R-bloggers. Author: Roger Oberg. If you’ve read the book “R for Data Science” or plan to, now you can dive deeper with co-author and RStudio Master Instructor Garrett Grolemund, winner of the Excellence in CE Award at JSM 2017! This two day workshop provides a comprehensive overview of what is now called the Tidyverse, a core set of R packages that are essential to Data Science. You will visualize, transform, and model data in R and work with date-times, character strings, and untidy data formats. Along the way, you will learn to use the brightest stars in the tidyverse: ... Read More

Feed: R-bloggers. Author: msuzen. Preamble There is a lot of confusion among practitioners regarding the concept of overfitting. It seems like, a kind of an urban legend or a meme, a folklore is circulating in data science or allied fields with the following statement:Applying cross-validation prevents overfitting and a good out-of-sample performance, low generalisation error in unseen data, indicates not an overfit. This statement is of course not true: cross-validation does not prevent your model to overfit and good out-of-sample performance does not guarantee not-overfitted model. What actually people refer to in one aspect of this statement is called overtraining. Unfortunately, this meme is not only ... Read More