Digital innovators will succeed because enterprise data doesn’t belong to silos and data has immense value, but only if available as a “whole”, to allow full picture of the enterprise rather than short term trends or baseline BI reports.
Also: Machine Learning Exercises in Python: An Introductory Tutorial Series; Introduction to Neural Networks, Advantages and Applications; The Internet of Things: An Introductory Tutorial Series; The BI & Data Analysis Conundrum: 8 Reasons Why Many Big Data Analytics Solutions Fail to Deliver Value
While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work. So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.
The class lectures include best practices of setting up a data mining project and preprocessing, going through a first sprint in R, using RStudio and packages like data.table, xgboost, trees and neural nets and caret.
In this series of post, the author will be presenting a set of Internet of Things technologies and applications in the form of tutorial in chapter form. Basic concepts are covered with an approachable style, not heaped in technical terms.
Learn to use analytics to deploy innovative business solutions at TDWI Big Data and Analytics for Business Advantage Summit, Oct 8-10 in Savannah, GA. Apply for this exclusive, complimentary event by Aug 11.
Classification is the process of categorizing or “classifying” some items into a predefined set of categories or “classes”. It is exactly the same even when a machine does so. Let’s dive a little deeper.
The creative aspects of machine learning are overshadowed by visions of an autonomous future, but machine learning is a powerful tool for communication. Most machine learning in today’s products is related to understanding.
This post presents a summary of a series of tutorials covering the exercises from Andrew Ng's machine learning class on Coursera. Instead of implementing the exercises in Octave, the author has opted to do so in Python, and provide commentary along the way.
We’d like to update you on RE•WORK’s Deep Learning Global Summit Series and the upcoming summits in London this September. We have an exclusive code to give KDnuggets readers discounted tickets to these events as well as on-demand content and expert interviews.
Neural networks work really well on many problems, including language, image and speech recognition. However understanding how they work is not simple, and here is a summary of unusual and counter intuitive properties they have.
Also: How GDPR Affects Data Science,AI and Deep Learning, Explained Simply; Are Most Machine Learning Experts Turning to Deep Learning?; Design by Evolution: How to evolve your neural network with AutoML
While about 60% of KDnuggets readers think AI and Automation will improve society, the optimism drops significantly among those with 4 or more years experience developing AI systems. Should we pay more attention to the experts?
The Saint Mary's College Master of Science in Data Science program will prepare you to enter into the data analysis process at any stage, from the initial formulation of the question, to visualizing data, to interpreting the results and drawing conclusions.
The Deep Learning Summit London and the AI Assistant Summit London will be continuing the RE•WORK Global Summit Series this September 21 & 22. Early Bird discount is ending on July 28th. Register now to guarantee a spot at the Summit and use the discount code KDNUGGETS to save 20% on all tickets.
Proteins are building blocks of all living matter. Although tremendous progress has been made, protein engineering remains laborious, expensive and truly complicated. Here is how Machine Learning can help.
This is a collection of 5 deep learning for natural language processing resources for the uninitiated, intended to open eyes to what is possible and to the current state of the art at the intersection of NLP and deep learning. It should also provide some idea of where to go next.
This article is just a reflection of my current understanding of the language of Deep Learning Meta Meta-Model. That’s definitely a mouth full, so to make life simpler for everyone, I just call this the Deep Learning Canonical Patterns.
Also: The Strange Loop in Deep Learning; How to Build a Data Science Pipeline; Automated Machine Learning - A Paradigm Shift That Accelerates Data Scientist Productivity; Medical Image Analysis with Deep Learning, Part 4
Coming European GDPR directive affects data science practice mainly in 3 areas: limits on data processing and consumer profiling, a “right to an explanation” for automated decision-making, and accountability for bias and discrimination in automated decisions.
Machine learning with Big Data is, in many ways, different than "regular" machine learning. This informative image is helpful in identifying the steps in machine learning with Big Data, and how they fit together into a process of their own.
Start with y. Concentrate on formalizing the predictive problem, building the workflow, and turning it into production rather than optimizing your predictive model. Once the former is done, the latter is easy.
A lot is changing in the world of marketing analytics. Marketing scientist Kevin Gray asks Professor Michel Wedel, a leading authority on this topic from the Robert H. Smith School of Business at the University of Maryland, what marketing researchers and data scientists most need to know about it.
There is a growing community around creating tools that automate many machine learning tasks, as well as other tasks that are part of the machine learning workflow. The paradigm that encapsulates this idea is often referred to as automated machine learning.
GANs (Generative Adversarial Networks), a type of Deep Learning networks, have been very successful in creating non-procedural content. This work explores the possibility of machine generated creative content.
As emerging technologies like AI/machine learning are adopted across different parts of the business, enterprises require a “digital brain” to coordinate those efforts and generate systemic intelligence.
This is the fourth installment of this series, and covers medical images and their components, medical image formats and their format conversions. The goal is to develop knowledge to help us with our ultimate goal — medical image analysis with deep learning.
This is a short list of 5 resources to help newcomers find their bearings when learning about self-driving vehicles, all of which are free. This should be sufficient to learn the basics, and to learn where to look next for further instruction.
Former academician and now Portugal MP Pedro Saraiva says that Parliaments and societies will improve if more people with a good statistical background become MP. Learn about the paradoxes and issues in statistics and politics.
We view EDA very much like a tree: there is a basic series of steps you perform every time you perform EDA (the main trunk of the tree) but at each step, observations will lead you down other avenues (branches) of exploration by raising questions you want to answer or hypotheses you want to test.
In a new whitepaper from Team Anaconda, Productionizing and Deploying Data Science Projects, our data science experts share the factors to consider when deploying data science projects, how to leverage Anaconda Project to encapsulate your data science projects, and more.
Research in economics and operations management posits that dynamic pricing is critically important when capacity is fixed (at least in the short run) and fixed costs represent a substantial fraction of total costs.
Also: Train your #deeplearning model faster and sharper — two novel techniques; Lecture Collection - Natural Language Processing with #DeepLearning (Winter 2017) [Stanford]; #ICYMI 10 Free Must-Read Books for #MachineLearning and #DataScience
Since 2009, Predictive Analytics World has delivered the heart of data science - driving predictive value from data - by featuring the most in-house leading practitioners from brand-name organizations. In 2017, we’ve lined up the best and brightest - and that can include you.
I was asked this question recently via LinkedIn message: "What advice would you give your younger data scientist self?" The best piece of advice I honestly think I can give is this: Forget about "data science."
Most people would have heard of the headline-making tremendous achievements in artificial intelligence (AI): Systems defeating world champions in board games like GO and winning quiz shows. These are small realizations of AI, but there is a silent revolution taking place in other areas, including Regulatory Compliance in Financial Services.
In this last post of the series, I describe how I used more powerful machine learning algorithms for the click prediction problem as well as the ensembling techniques that took me up to the 19th position on the leaderboard (top 2%)
NumPy receives first ever funding, thanks to Moore Foundation; Cheat Sheets for deep learning and machine learning; How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow & Keras; Andrej Karpathy leaves OpenAI for Tesla; Machine, a machine learning IDE
Top 10 Quora Machine Learning Writers and Their Best Advice, Updated, Applying Deep Learning to Real-world Problems; Using the TensorFlow API: An Introductory Tutorial Series; Text Clustering: Get quick insights from Unstructured Data; Why Artificial Intelligence and Machine Learning?