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).”
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.
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.
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, 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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?
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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!
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 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.
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.
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
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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?
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
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.
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.
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.
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!
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.
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.
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.
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.