01:10
gan example
18:54
DS4PH Git github and version control
10:05
How I'm using Zoom for teaching
05:04
Followup on zoom for teaching, what the recordings page looks like
13:26
04 Basic regression using pytorch
07:13
03 What are those NN diagrams 3
06:24
02 What are those neural network diagrams 2
05:15
01 Neural network diagrams, getting started
13:31
On causation and manipulation
07:15
The geometric mean and logging data
07:45
Bayesianism versus Frequentism rediscussed via statistical pragmatism
03:53
How to create and serve a simple web page with github pages.
11:07
Getting gigantum running on a digital ocean server
21:23
Deep learning in public health and personalized medicine
10:49
Methods in Biostatistics with R
00:44
Methods in Biostatistics with R short intro 720HD
07:08
A trick for running Rstudio on paperspace through SSH
04:25
How to use docker to run an rustdio server on digital ocean
14:38
On applying to and accepting offers from PhD/Master's programs
04:57
Trying out gnu-root debian
09:54
Why are P-values uniformly distributed under H0?
02:30
Try out the new Rstudio server
07:20
Will AI eat statistics?
02:14
Silly little tip, register your IP with a DNS hosting service
04:41
How can I get started in Bayesian data analysis?
16:46
Setting up keras, rstudio server and shiny
07:24
What math prereqs do I need for biostat grad school; how long will it take me to get them
02:17
Why aren't you settings your chrome settings as a search engine?
10:36
Clearing the docket
05:27
How to embed a shiny app
07:49
What do people mean when they say a statistic is "Robust to normality"?
07:15
R versus Python, sort of, not really
05:12
Should I rescale my regression variables?
06:31
Post model selection inference
10:26
What R IDE should I use
04:40
Screencastify
05:40
Review of Jeff Leek's blog post on chromebook data science
06:56
Which regression
06:52
Review of wevideo
06:09
What are GAMs
08:55
What is GEE (Episode 27)
07:48
Short evaluation of Paperspace
09:25
What is a P-Value? (episode 24)
04:54
What should I use to serve R applications over the internet? (episode 23)
10:58
Week 22 What are the steps of a data science experiment
06:23
Week 21 Is regression an ML technique?
14:33
Week 20 Setting Up an Rstudio Server on Digital Ocean or EC2
06:20
Week 18 the future of MOOCs
06:32
Week 17, Mediation versus Moderation
05:56
week 16 will data science eventually take over the field of statistics?
04:45
week 15 what mathematics should I know to apply to a stats/biostats PhD program?
07:35
Week 14, professors and startups
08:58
Week 13, robust variance estimation
07:25
Week 12 what is Chi-Squared testing / Filtering
08:17
Week 11, question olio
09:04
Week 10 What's too big? Also, excuses for no video last week
10:52
Week 9 Machine Learning versus classical statistics
10:44
Week 8, should I get a PhD, if so in what?
13:14
Week 7 R versus Excel
06:51
Week 6, IID assumptions demystified, sort of
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