2016 Dechttp likes 41
All (100) | Courses, Education (9) | Meetings (11) | News, Features (26) | Opinions, Interviews (31) | Software (3) | Tutorials, Overviews (18) | Webcasts & Webinars (2)
- Citizen Data Scientist, Jumbo Shrimp, and Other Descriptions That Make No Sense - Dec 30, 2016.
No one would say “Citizen Lawyer” or “Citizen Nuclear Physicists” or “Citizen Physician.” I guess a “Citizen Physician” would be someone who “practices medicine but whose primary job function is outside of the field of medicine (meaning that they’ve had no training in medicine or medical procedures).”
- Game Theory Reveals the Future of Deep Learning - Dec 29, 2016.
This post covers the emergence of Game Theoretic concepts in the design of newer deep learning architectures. Deep learning systems need to be adaptive to imperfect knowledge and coordinating systems, 2 areas with which game theory can help.
- Laying the Foundation for a Data Team - Dec 28, 2016.
Admittedly, there is a lot more to building a successful data team, and we would be lying if we pretended we have it all figured out. But hopefully focusing on the elements in this post is a good start.
- A Funny Look at Big Data and Data Science - Dec 27, 2016.
A less than serious look at Big Data and Data Science. If you can laugh at all cartoons, then your Data Science skills are in good shape.
- I’m A Data Guy And I Don’t Get Why Everyone’s Obsessed With Data - Dec 27, 2016.
In post after post, in article after article, battle-lines are drawn between Big Ideas and Big Data. I really don't get what the fuss is about. What's wrong with having both?
- Top Stories, Dec 19-25: Machine Learning & AI: Main Developments in 2016 and Key Trends in 2017; 4 Reasons Your Machine Learning Model is Wrong - Dec 27, 2016.
Machine Learning & AI: Main Developments in 2016 and Key Trends in 2017; 4 Reasons Your Machine Learning Model is Wrong (and How to Fix It); 50+ Data Science, Machine Learning Cheat Sheets; First Deep Learning for coders MOOC
- Predictive Analytics World for Manufacturing, Dusseldorf, Feb 2-3 - Dec 27, 2016.
Predictive Analytics World, the leading vendor-independent expert conference, launches the first European edition of PAW Manufacturing in Dusseldorf, Germany. Read a preview of Process Mining for the Internet of Events. Register by Jan 13 and save with code KDN15.
- How I Detect Fake News, by Tim O’Reilly - Dec 26, 2016.
Read how Tim O'Reilly traced the falsity of one internet meme, and what that teaches us about how an algorithm might do it.
- Internet of Things (IoT) Challenge: The Sensor That Cried Wolf - Dec 23, 2016.
William Schmarzo, the "Dean of Big Data," shares a personal story that identifies a tangible issue related to technology in general, and which carries an important message for the Internet of Things (IoT) in particular.
- Your holiday discount for data science learning (until Dec 26) - Dec 23, 2016.
Until Monday, Dec 26, Springboard has special promotion for KDnuggets readers: $100 off all of data science and analytics courses with the promo code HAPPYHOLIDAYS! Learn more.
- Over 600 data science, machine learning, Big Data eBooks/videos for only $5 (until Jan 9) - Dec 22, 2016.
Packt have more than 600 data science, analysis, machine learning and Big Data eBooks and video courses. And until Jan 9, 2017 every single one is available for just $5.
- The Five Capability Levels of Deep Learning Intelligence - Dec 22, 2016.
Deep learning writer Carlos Perez gives his own classification for deep learning-based AI, which is aimed at practitioners. This classification gives us a sense of where we currently are and where we might be heading.
- The big data ecosystem for science: Climate Science and Climate Change - Dec 22, 2016.
Climate change is one of the most pressing challenges for human society in the 21st century. We review the Big Data ecosystem for studying the climate change.
- First Deep Learning for coders MOOC launched by Jeremy Howard - Dec 21, 2016.
Leading Data Scientist and entrepreneur Jeremy Howard launches a free Deep Learning course that shows end-to-end how to get state of the art results, including a top place in a Kaggle competition.
- 3 ways to learn Data Science at Statistics.com - Dec 21, 2016.
Get the personal touch you need to deepen your learning with Statistics.com classes that are small, rich and engaging with readings, videos, quizzes, homework, and practical projects, taught online by leading instructors.
- Top KDnuggets tweets, Dec 14-20: False positives versus false negatives: Best explanation ever - Dec 21, 2016.
Also #MachineLearning, #AI experts: Main Developments 2016, Key Trends 2017; Official code repository for #MachineLearning with #TensorFlow book; Top 10 Essential Books for the #Data Enthusiast.
- Privacy, Security and Ethics in Process Mining - Dec 21, 2016.
Data Privacy, Security and Ethics are hot yet complex topics in the business and data science world. This important article talks about and provide guidelines for privacy, security and ethics, specifically in the context of Process Mining.
- 4 Reasons Your Machine Learning Model is Wrong (and How to Fix It) - Dec 21, 2016.
This post presents some common scenarios where a seemingly good machine learning model may still be wrong, along with a discussion of how how to evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall.
- Data Science Basics: Power Laws and Distributions - Dec 21, 2016.
Power laws and other relationships between observable phenomena may not seem like they are of any interest to data science, at least not to newcomers to the field, but this post provides an overview and suggests how they may be.
- Data Sources for Cool Data Science Projects - Dec 20, 2016.
One of the biggest obstacles to successful projects has been getting access to interesting data. Here are some more cool public data sources you can use for your next project.
- 3rd Annual Global Predictive Analytics Conference, Santa Clara, March 27-29 – KDnuggets Offer - Dec 20, 2016.
Global Predictive Analytics Conference emphasizes sharing real world experiences, creating a balanced big predictive analytics team, new methods used across multiple industry verticals, and more. Use code KDNUGGETS to save.
- Mark van Rijmenam’s Top 7 Big Data Trends for 2017 - Dec 20, 2016.
Top Big Data expert Mark van Rijmenam weighs in with his top Big Data-related predictions for 2017.
- Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017 - Dec 20, 2016.
As 2016 comes to a close and we prepare for a new year, check out the final instalment in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.
- Supercharge Your Data Science Team, Dec 21 Webinar - Dec 20, 2016.
On December 21st, Continuum Analytics CTO Peter Wang will share how you can supercharge your Data Science team by simplifying the building process for even the most complicated dashboards and display streaming data in real time.
- New Poll: Can You Live with Ethics of Machine Learning and Self-Driving Cars? - Dec 19, 2016.
The difficult thing about Machine Learning Ethics is that it forces us to consider the harsh choices people sometimes have to make but don't want to think about. Here is one such situation - what is the right choice? Please vote.
- 3rd Annual Global Data Science Conference, Santa Clara, March 27-29, 2017 - Dec 19, 2016.
Get ready for one of the leading and vendor agnostic Global Big Data Conference, happening at Santa Clara, CA on March 27-29 2017. Register now with promo code: KDNUGGETS and save upto $200.
- Top Stories, Dec 12-18: Data Science, Predictive Analytics 2016 Developments, 2017 Key Trends; 50+ Data Science, Machine Learning Cheat Sheets - Dec 19, 2016.
50+ Data Science, Machine Learning Cheat Sheets, updated; Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017; Bayesian Basics, Explained; Data Analytics Models in Quantitative Finance and Risk Management
- What we can learn from AI mistakes - Dec 19, 2016.
Because of recent innovations and research in AI, we have seen AI performing best in some very important tasks and even worst in even simple tasks. So the question is, Why is it that AI can look so brilliant and so stupid at the same time?
- Predictions for Deep Learning in 2017 - Dec 19, 2016.
The first hugely successful consumer application of deep learning will come to market, a dominant open-source deep-learning tool and library will take the developer community by storm, and more Deep Learning predictions.
- ResNets, HighwayNets, and DenseNets, Oh My! - Dec 19, 2016.
This post walks through the logic behind three recent deep learning architectures: ResNet, HighwayNet, and DenseNet. Each make it more possible to successfully trainable deep networks by overcoming the limitations of traditional network design.
- Data Science & Ancestry - Dec 17, 2016.
Ancestry is curious topic for many people to find out their origin and history. Today, data science is used to help these people to dig into their family history and build the family trees.
- Industry Predictions: Key Trends in 2017 - Dec 16, 2016.
With 2017 almost upon us, KDnuggets brings you opinions from industry leaders as to what the relevant and most important 2017 key trends will be.
- The 5 Basic Types of Data Science Interview Questions - Dec 16, 2016.
Data science interviews are notoriously complex, but most of what they throw at you will fall into one of these categories.
- Big Data Bootcamp, Santa Clara, Mar 27-29, 2017 - Dec 16, 2016.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape targeted towards both technical and non-technical people who want to understand the emerging world of Big Data. Use code KDNUGGETS to save.
- Deep Learning Works Great Because the Universe, Physics and the Game of Go are Vastly Simpler than Prior Models and Have Exploitable Patterns - Dec 16, 2016.
How is Deep Learning experiencing such success solving complex problems? Deep Learning is useful and powerful but it is also that the problems were not as big or as hard as researchers feared when they were unsolved.
- Introduction to Bayesian Inference - Dec 16, 2016.
Bayesian inference is a powerful toolbox for modeling uncertainty, combining researcher understanding of a problem with data, and providing a quantitative measure of how plausible various facts are. This overview from Datascience.com introduces Bayesian probability and inference in an intuitive way, and provides examples in Python to help get you started.
- Ready for a Mathematically Rigorous Data Science Master’s Program? - Dec 16, 2016.
In Saint Mary's College's primarily online Master of Science in Data Science program you will develop a strong mathematical base that will allow you to take on complex data challenges now and in the future, no matter what programming language you're using.
- Top 10 Amazon Books in Databases & Big Data, 2016 Edition - Dec 15, 2016.
Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the Databases & Big Data category.
- Big Data Bootcamp, Santa Clara, Jan 20-22, 2017 - Dec 15, 2016.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape targeted towards both technical and non-technical people who want to understand the emerging world of Big Data. Special KDnuggets discount.
- Top 2016 KDnuggets Stories: Must-Know Data Science Interview Q&A, 10 Algorithms Machine Learning Engineers Need to Know - Dec 15, 2016.
Also 20 Questions to Detect Fake Data Scientists; Software used for Analytics, Data Science, Machine Learning projects; Top Algorithms and Methods Used by Data Scientists
- Artificial Intelligence and Life in 2030 - Dec 15, 2016.
Read this engaging overview of a report from the Stanford University 100 year study of Artificial Intelligence, “a long-term investigation of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society.”
- The big data ecosystem for science: Genomics - Dec 15, 2016.
The field of genomics has undergone a revolution over the past decade as the cost of sequencing has rapidly declined and the practice of sequencing has been commoditized. We review the Big Data ecosystem in genomics.
- Top KDnuggets tweets, Dec 7-13: Want to learn Numpy? A Github repo of Numpy learning exercises - Dec 14, 2016.
Also Deep Learning Roadmap: "Which paper should I start reading from?"; Free ebooks: #MachineLearning with #Python and Practical Data Analysis; Daily plan for studying to become a Google software engineer.
- 50+ Data Science, Machine Learning Cheat Sheets, updated - Dec 14, 2016.
Gear up to speed and have concepts and commands handy in Data Science, Data Mining, and Machine learning algorithms with these cheat sheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark, Matlab, and Java.
- The Costs of Misclassifications - Dec 14, 2016.
Importance of correct classification and hazards of misclassification are subjective or we can say varies on case-to-case. Lets see how cost of misclassification is measured from monetary perspective.
- AI, Analytics, IoT, Blockchain: New York Life, Chubb and Assurant discuss technology integration - Dec 14, 2016.
AI, Analytics, IoT, Blockchain – do you know how all of this will fundamentally impact insurance? Get exclusive white paper based on private interviews with New York Life, Chubb and Assurant discussing the role of ever-changing insurance technology to their business.
- What You Are Too Afraid to Ask About Artificial Intelligence (Part II): Neuroscience - Dec 14, 2016.
As a followup to Part 1, which covered the advancements in machine learning, this post gives considers comprehension of the brain mechanisms. We have an ever-increasing understanding of brain processes, which may help to foster the development of an AGI.
- Data Science Basics: What Types of Patterns Can Be Mined From Data? - Dec 14, 2016.
Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.
- The PAW 2017 Predictive Analytics Conference Calendar - Dec 13, 2016.
PAW conference series covers applying predictive analytics to business, financial services, government, healthcare, manufacturing, and workforce. Save the dates for upcoming conferences around the world and save with code KDN150.
- Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017 - Dec 13, 2016.
Key themes included the polling failures in 2016 US Elections, Deep Learning, IoT, greater focus on value and ROI, and increasing adoption of predictive analytics by the "masses" of industry.
- Data Analytics Models in Quantitative Finance and Risk Management - Dec 13, 2016.
We review how key data science algorithms, such as regression, feature selection, and Monte Carlo, are used in financial instrument pricing and risk management.
- Achieving Human Parity in Conversational Speech Recognition - Dec 13, 2016.
This is an overview of the paper which outlines, for the first time, a system has been developed that exceeds human performance in one of the most difficult of all human speech recognition tasks: natural conversations held over the telephone.
- Global AI Conference, Santa Clara, Jan 19-21 2017 - Dec 13, 2016.
Get up to speed on emerging AI technologies, develop new technical skills, learn best practices at vendor-agnostic Global Artificial Intelligence Conference, Jan 19-21, 2017 in Santa Clara. Use code KDNUGGETS to register and save.
- Forecast Your Future With Analytics Insights From Google, Bloomberg & others - Dec 13, 2016.
What are the top AI & Machine Learning trends for 2017? Join the Predictive Analytics Innovation Summit in San Diego on Feb 22 & 23, 2017, to find out everything you need to know about Real-time Machine Learning algorithms, developing strong data-driven cultures and more!
- arXiv Paper Spotlight: Why Does Deep and Cheap Learning Work So Well? - Dec 13, 2016.
The recent paper at hand approaches explaining deep learning from a different perspective, that of physics, and discusses the role of "cheap learning" (parameter reduction) and how it relates back to this innovative perspective.
- Springboard launches data science bootcamp with a job guarantee - Dec 12, 2016.
Springboard Data Science Career Track is the first online data science bootcamp that offers a job guarantee to its graduates. Springboard tracked 50 graduates and saw that all got a job within 6 months, with a median increase of $18,000 in first-year salary.
- New Book: TensorFlow for Machine Intelligence – KDnuggets Holiday Offer - Dec 12, 2016.
TensorFlow for Machine Intelligence is a hands-on introduction to learning algorithms and the "TensorFlow book for humans." For a limited holiday special, KDnuggets readers get a 40% discount, available here.
- Top Stories, Dec 5-11: Why Deep Learning is Radically Different From Machine Learning; Data Science Trends To Look Out For In 2017 - Dec 12, 2016.
Why Deep Learning is Radically Different From Machine Learning; Data Science Trends To Look Out For In 2017; Bayesian Basics, Explained; Big Data: Main Developments in 2016 and Key Trends in 2017; 4 Cognitive Bias Key Points Data Scientists Need to Know
- Just How Smart Are Smart Machines? - Dec 12, 2016.
The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities. Here we examine 4 levels of intelligence across task types.
- Data Science and Big Data: Definitions and Common Myths - Dec 12, 2016.
A well-set data strategy is becoming fundamental to every business, regardless the actual size of the datasets used. However, in order to establish a data framework that works, there are a few misconceptions that need to be clarified.
- Where Analytics, Data Mining, Data Science were applied in 2016 - Dec 12, 2016.
CRM/Consumer Analytics, Finance, and Banking are still the leading applications, but Anti-spam, Mobile apps, Travel/hospitality grew the most in 2016. Share of Health care, Consumer analytics, and Direct Marketing/ Fundraising data science applications declined for 2 years in a row.
- Bayesian Basics, Explained - Dec 9, 2016.
This interview between Professor Andrew Gelman of Columbia University and marketing scientist Kevin Gray covers the basics of Bayesian statistics and how it differs from the ordinary statistics most of us learned in college.
- 4 Cognitive Bias Key Points Data Scientists Need to Know - Dec 9, 2016.
Cognitive biases are inherently problematic in a variety of fields, including data science. Is this something that can be mitigated? A solid understanding of cognitive biases is the best weapon, which this overview hopes to help provide.
- Introduction to K-means Clustering: A Tutorial - Dec 9, 2016.
A beginner introduction to the widely-used K-means clustering algorithm, using a delivery fleet data example in Python.
- Chief Data & Analytics Officer Winter, Miami, January 25-26 - Dec 9, 2016.
A one and a half day exclusive event for the who’s who in the world of Analytics, we will be welcoming 100+ senior analytics leaders. Save 20% with code KDW20.
- Artificial Neural Networks (ANN) Introduction, Part 2 - Dec 9, 2016.
Matching the performance of a human brain is a difficult feat, but techniques have been developed to improve the performance of neural network algorithms, 3 of which are discussed in this post: Distortion, mini-batch gradient descent, and dropout.
- KDnuggets Top Blogs and Bloggers in November 2016 - Dec 8, 2016.
We recognize the best KDnuggets Bloggers who had the most popular blogs by views or social media shares in November 2016.
- Predictive Analytics for the Enterprise - Dec 8, 2016.
Data science success in the big data era does not start with data and software. Organizations intent upon being data-driven leaders start with immersive live training and transformative strategy.
- The big data ecosystem for science: Physics, LHC, and Cosmology - Dec 8, 2016.
Big Data management is essential for experimental science and technologies used in various science communities often predate those in Big Data industry and in many cases continue to develop independently. This post highlights some of these technologies, focusing on those used by several projects supported by the National Energy Research Scientific Computing Centre (NERSC).
- Artificial Neural Networks (ANN) Introduction, Part 1 - Dec 8, 2016.
This intro to ANNs will look at how we can train an algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.
- Data Science Trends To Look Out For In 2017 - Dec 8, 2016.
Machine Learning is here to stay, with more firms following Google and Facebook in the race to attract the best machine learning experts and Data Scientists. We also see a merger of IoT and Data Science. Read on for more trends.
- Navigating the World of Big Data Analytics - Dec 8, 2016.
Fulcrum Agile Analytics Lab- helps our partners test new technologies, new methodologies and new data sets quickly in an environment that can scale up and down and that meets all of their security and compliance requirements. Read to learn more and schedule a consultation.
- Top KDnuggets tweets, Nov 30 – Dec 06: A great and useful collection of minimal and clean implementations of #MachineLearning algorithms - Dec 7, 2016.
Also: #MachineLearning Yearning book draft, Free Download, by Andrew Ng; A short guide to learn #NeuralNets, and maybe get famous and rich with #DeepLearning; Free Book: Foundations of Computer Science, Aho & Ullman.
- The Best Metric to Measure Accuracy of Classification Models - Dec 7, 2016.
Measuring accuracy of model for a classification problem (categorical output) is complex and time consuming compared to regression problems (continuous output). Let’s understand key testing metrics with example, for a classification problem.
- What You Are Too Afraid to Ask About Artificial Intelligence (Part I): Machine Learning - Dec 7, 2016.
In the first of a 2 part series, this post will briefly discuss major advancements in pure machine learning techniques - while a followup post will similarly treat neuroscience - both with an agnostic lens.
- arXiv Paper Spotlight: Automated Inference on Criminality Using Face Images - Dec 7, 2016.
This recent paper addresses the use of still facial images in an attempt to differentiate criminals from non-criminals, doing so with the help of 4 different classifiers. Results are as troubling as they are unsettling.
- R-Brain Platform for Data Science: R, Python, sharing, security, and marketplace - Dec 7, 2016.
R-Brain IDE enables data scientists to use both R and Python with full language support. It enables sharing and has a marketplace for models. Try it free.
- Top November Stories: Trump, Failure of Prediction, and Lessons for Data Scientists - Dec 6, 2016.
Also: How Bayesian Inference Works; Top 20 Python Machine Learning Open Source Projects, updated; Machine Learning vs Statistics
- Internet of Things Tutorial: Introduction - Dec 6, 2016.
In the first of a series of posts, a set of Internet of Things technologies and applications are presented. Following posts will expand on these topics in tutorial form. Get an introduction to IoT here.
- Big Data: Main Developments in 2016 and Key Trends in 2017 - Dec 6, 2016.
As 2016 comes to a close and we prepare for a new year, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2016 and their 2017 key trend predictions.
- Webinar: Predictive Analytics: Failure to Launch – Dec 14 - Dec 6, 2016.
Learn how to get started with predictive modeling and overcome strategic and tactical limitations that cause data mining projects to fall short of their potential. Next webinar is Dec 14.
- Join Data Leaders in London this February – KDnuggets Offer - Dec 6, 2016.
Join Europe's Data Leaders in London at Chief Data Officer Europe, 20-23 February or at Chief Analytics Officer Europe, 25-27 April. Special KDnuggets discount until 31 December 2016.
- TDWI Analytics, Big Data, Data Science Training: Las Vegas, February - Dec 5, 2016.
TDWI Las Vegas, Feb 12-17 is the leading event for Analytics, Big Data, Data Management and Data Science training, bringing together the brightest minds in data to share their expertise and insights. Choose from 5 core learning tracks, TDWI Leadership Summit, or Data Science Bootcamp.
- Free ebooks: Machine Learning with Python and Practical Data Analysis - Dec 5, 2016.
Two free ebooks: "Building Machine Learning Systems with Python" and "Practical Data Analysis" will give your skills a boost and make a great start in the New Year.
- Kobielus Predictions for Data Science in 2017 - Dec 5, 2016.
IBM Data Evangelist James Kobielus predictions for 2017, including key role of data scientists in survival of their companies. Join industry experts for a live #MakeDataSimple Crowdchat on Thursday December 8 at 1:00pm EST.
- CDO: to stay or to go? - Dec 5, 2016.
The Chief Digital Officer role has grown 1000-fold in the last 9 years, but will it remain popular in 2025? We examine the parallels between the electric and digital revolutions.
- Top Stories, Nov 28-Dec 4: Machine Learning vs Statistics; Hard Thing About Deep Learning; Developers’ Machine Learning Intro - Dec 5, 2016.
Machine Learning vs Statistics; The hard thing about deep learning; Top 20 Python Machine Learning Open Source Projects, updated; Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed; Introduction to Machine Learning for Developers
- Why Deep Learning is Radically Different From Machine Learning - Dec 5, 2016.
There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference?
- Top /r/MachineLearning Posts, November: StarCraft II for AI Research; Google AI Experiments Website; Google in Montreal - Dec 5, 2016.
DeepMind and Blizzard to release StarCraft II as an AI research environment; Google AI Experiments Website; Google opens new Montreal-based AI research lab; Lip Reading Sentences in the Wild; Clean implementations of machine learning algorithms
- How To Make Your Mark As A Woman In Big Data - Dec 3, 2016.
Despite the shift in big data technology innovation that is driving tremendous growth and opportunities, women still play a small role in this arena. Here are 5 thoughts for women considering a career in big data.
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in November 2016 - Dec 2, 2016.
Faculty/Postdoc positions in Data Science/Machine Learning at DePaul, UCSB, Virginia Tech, Barcelona U, Aarhus U, Georgia State U, U. of Innsbruck, Drexel, CMU, Oregon State U, Iowa State, and more.
- Upcoming Meetings in Analytics, Big Data, Data Mining, Data Science: December 2016 and Beyond - Dec 2, 2016.
Coming soon: IEEE Big Data, Big Data/Business Analytics Innovation Summits Las Vegas, AnacondaCON Austin, WSDM 2017 Cambridge, TDWI Las Vegas, and more.
- Interviews with Data Scientists: Claudia Perlich - Dec 2, 2016.
In this wide-ranging interview, Roberto Zicari talks to a leading Data Scientist Claudia Perlich about what they must know about Machine Learning and evaluation, domain knowledge, data blending, and more.
- Smart Data Platform – The Future of Big Data Technology - Dec 2, 2016.
Data processing and analytical modelling are major bottlenecks in today’s big data world, due to need of human intelligence to decide relationships between data, required data engineering tasks, analytical models and it’s parameters. This article talks about Smart Data Platform to help to solve such problems.
- Random Forests® in Python - Dec 2, 2016.
Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. This is a post about random forests using Python.
- Why the Pros Go To Predictive Analytics World - Dec 2, 2016.
Predictive Analytics World for Business is May 14-18, 2017. Save big on full conference passes when you register today with super early bird rates, plus save an extra $150 when you use code KDN150 at checkout!
- The hard thing about deep learning - Dec 1, 2016.
It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.
- Data Science Deployments With Docker - Dec 1, 2016.
With the recent release of NVIDIA’s nvidia-docker tool, accessing GPUs from within Docker is a breeze. In this tutorial we’ll walk you through setting up nvidia-docker so you too can deploy machine learning models with ease.
- Online Master of Science in Predictive Analytics. - Dec 1, 2016.
Build hot skills for the growing analytics field, learn key statistical concepts and practical applications from distinguished Northwestern faculty and industry experts and prepare for leadership level career. Spring application deadline Jan 15.
- Top Reasons Why Big Data, Data Science, Analytics Initiatives Fail - Dec 1, 2016.
We examine the main reasons for failure in Big Data, Data Science, and Analytics projects which include lack of clear mandate, resistance to change, and not asking the right questions, and what can be done to address these problems.