Sunday, July 29. 2012
Link roundup, week 30/2012
-
David MacKay: Information Theory, Pattern Recognition and Neural Networks: The Book
-
Information Theory, Inference, and Learning Algorithms
-
640 pages, Published September 2003
-
PDF (A4) pdf (9M) (fourth printing, March 2005)
-
-
Cookbook for R ยป Cookbook for R
HTML
-
formerly named R Cookbook
-
It is not related to Paul Teetor’s excellent R Cookbook
-
-
Big Data -- Why the 3Vs Just Don’t Make Sense -- TDWI -The Data Warehousing Institute
When are we done defining big data? p.1
-
settled on 3 Vs -- volume, variety, and velocity
-
another V: value
-
if big data is understood solely on the basis of these trends, it isn’t clear that it’s at all hype-worthy
-
if “big data” simply describes the volume, variety, and velocity of the information that constitutes it, our existing data management practices are still arguably up to the task
-
big data is hyped on the basis of its real or imagined outputs
-
a lot more interesting when you bring in ‘V’ for value
-
-
No, Really, Some of My Best Friends Are Data Scientists
When are we done defining data science?
-
the skills of a “data scientist” are those of a modern statistician
-
know how to move data around and manipulate data with some programming language
-
know how to draw informative pictures of data
-
Knowledge of stats, errorbars, confidence intervals
-
try to get people from different backgrounds
-
Great communication skills
-
a lot of what we teach The Kids now looks a lot more like machine learning than statistics as it was taught circa 1970, or even circa 1980
-
Everything I know about statistics I’ve learned without formal instruction
-
is not, in my experience, intrinsically hard for anyone who already has a decent grounding in some other mathematical science
-
mastering them really does mean trying to do things and failing
-
potentially hazardous. This is the idea that all that really matters is being “smart”
-
counter-productive for students to attribute their success or failure in learning about something to an innate talent
-
-
Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley & SAS Business): Bill Franks
Bill Franks is Chief Analytics Officer at Teradata
-
27. April 2012
-
336 Seiten
-
-
The Big Question In Big Data Is...What’s The Question? | SmartData Collective
-
Some folks like to confuse Hadoop with big data
-
Focus On the Questions To Ask, Not The Answers
-
The failure of data warehouses to provide real-time data led to the creation of data marts
-
Data marts failed to provide complete and updated and comprehensive views
-
existing solutions still don’t solve the problem. Why? The market and business environment have changed
-
Data moves from structured to unstructured. Sources exponentially proliferate. Data quality is paramount.
-
Real-time is irrelevant because speed does not trump fidelity. Quantity does not trump quality
-
Business questions remained unanswered despite the massive number of reports and views and charts
-
The big shift is about moving from data to decisions
-
-
Information Theory, Pattern Recognition, and Neural Networks
-
Draft videos (editing incomplete)
-
Entropy and Data Compression
-
Shannon’s Source Coding Theorem
-
Inference and Information Measures for Noisy Channels
-
Introduction to Bayesian Inference
-
Approximating Probability Distributions
-
Clustering
-
Monte Carlo Methods
-
Gibbs sampling
-
Neural Networks
-
Categories
Follow me
