Study Hacks » Blog Archive » “Being Very Good at Anything Involves Being Somewhat Addicted”: Hard Truth on the Sheer Difficulty of Making an Impact
10 weeks ago
I am increasingly stricken by the yawning gap that exists between the feel-good, follow your passion, be the change you want to see-style chatter that fills the online world, and the reality of how people actually end up making a true impact.
study-hacks
dedication
diligence
motivational
work-hard
10 weeks ago
Teacher Builds on the Basics
11 weeks ago
Article about the great teacher, Yaser Abu-Mostafa, from CalTech.
teaching
motivational
pattern-recognition
machine-learning
11 weeks ago
Virtual Laboratories in Probability and Statistics
11 weeks ago
Excellent resource on introductory math stat, including Urn models and Discrete Distributions. NOTE: I found this site when I was first learning clustering evaluation and considered it from an urn model perspective.
mathematical-statistics
statistics
tutorial
urn-models
discrete-distributions
11 weeks ago
Finite Sampling Models
11 weeks ago
Excellent resource on Urn models and Discrete Distributions. NOTE: I found this site when I was first learning clustering evaluation and considered it from an urn model perspective.
statistics
mathematical-statistics
urn-models
discrete-distributions
tutorial
11 weeks ago
An action plan for data science, a decade ago
march 2012
A discussion about data science and William Cleveland's initial definition and paper.
data-science
statistics
data
visualization
machine-learning
march 2012
Active Learning Challenge
february 2012
The site contains several data sets that are used to benchmark active learning methods. A thorough discussion is given on how to evaluate active learning methods for the given data sets.
machine-learning
active-learning
datasets
data-competition
benchmark
february 2012
An Introduction to the Interactive Debugging Tools in R (PDF)
february 2012
An excellent overview of debugging in R. I was unfamiliar with all of the ways browser() and debug() could be used before reading this document; not only are they useful for tracking down syntax errors, they are helpful in tracking down logic errors.
R
debugging
february 2012
active-learning
advice
amazon
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bash
bayes
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big-data
bioconductor
bioinformatics
biostatistics
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C
classification
clustering
code
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command-line
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deep-learning
diligence
dimension-reduction
discrete-distributions
emacs
ggplot2
gibbs-sampling
git
gtd
hadoop
HPC
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java
latex
lattice
linear-algebra
machine-learning
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mcmc
memory-management
motivational
multivariate
neural-networks
notes
octopress
org-mode
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PCA
plots
prediction
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rhipe
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urn-models
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