This is a nice intro to NPM.
And here’s a nice intro to NPM.
This post captures some difference between npm and bower:
This is a nice intro to NPM.
And here’s a nice intro to NPM.
This post captures some difference between npm and bower:
This link gives a nice summary of data visualization libraries using D3:
Interestingly, it mentions mermaid and rickshaw! Two cool libraries I recently came across
Real time:
References:
In one of my previous posts, I had noted my thoughts around statistical measures like standard deviation and confidence intervals.
The fun part is of course when one has to debug these measures.
To that end I developed some insights by trying to visualize the data and plotting different kinds of charts using matplotlib
Also, sometimes you have to debug plots when they make no sense at all. Like this one below:
Code:
As part of my previous post on confidence intervals, I wanted to now plot the error bars that I got from my dataset.
I had to play around a bit to get this going. The referenced links helped me in this effort.
Tip:
References:
(additional references which might be useful)
Code:
We have a system running Multi-Armed Bandit.
So when it came to select the next generation of ML algo to try out, we had a few choices:
Interestingly, on the dataset I was using I didn’t see much of a difference in algorithmic performance across the 4 different algorithms above.
Code:
A very interesting problem in ML is : What to try next ? Andrew Ng has some very interesting insights on this topic. (See the reference section below)
What to Try next ? |
Underfit (high bias) |
Overfit (high variance) |
Getting More Training Examples |
No |
Yes |
Try smaller set of features |
No |
Yes. But first see if you can get more training examples. |
Additional features |
Yes |
Maybe. If we get a feature that gives a strong signal then yes add it. But also invest in more data collection in parallel. |
Code:
References:
Recently did some fun visualizations with horizontal bar. Check it out:
The purpose was to understand how to reduce redis latencies in the presence of a large number of key lookups.
There are two ways :
(1) using pipeline
(2) hash data structure. Redis has atomic commands (HINCRBY) to increment the value of the hash yet, which is super cool !
Code:
References:
Learnt several few things from this hack:
[1] How to do XML parsing in Python. (blogged about it as well)
[2] Visualization.
[3] Python Coding. Joel’s code is amazing.
Some cool visualizations are as follows:
Here’s how the data looks like plotted onto the US map:
Check out how the variation when the value of K varies from K=1 to K=5.
K=1. This is an example of overfitting.
K=5
Code: