Daily Shaarli

All links of one day in a single page.

08/13/18

Sparse matrix representations in scipy | Eric Heydenblog

Introduction to sparse matrices.

A sparse matrix is just a matrix that is mostly zero. Typically, when people talk about sparse matrices in numerical computations, they mean matrices that are mostly zero.

Everything you can do with a time series

Aim

Since my first week on this platform, I have been fascinated by the topic of time series analysis. This kernel is prepared to be a container of many broad topics in the field of time series analysis. My motive is to make this the ultimate reference to time series analysis for beginners and experienced people alike.

Some important things

  1. This kernel is a work in progress so every time you see on your home feed and open it, you will surely find fresh content.
  2. I am doing this only after completing various courses in this field. I continue to study more advanced concepts to provide more knowledge and content.
  3. If there is any suggestion or any specific topic you would like me to cover, kindly mention that in the comments.
  4. If you like my work, be sure to upvote(press the like button) this kernel so it looks more relevant and meaningful to the community.
Eric Jang: Dijkstra's in Disguise

You can find a PDF version of this blog post here . A weighted graph is a data structure consisting of some vertices and edges, and each...

Crossing the Chasm - Wikipedia
Rust concurrency patterns: communicate by sharing your Sender

Doing concurrency in ‘share by communicating’ style has been popularized by the Go community. It’s a valuable approach to concurrency in…

11 Classical Time Series Forecasting Methods in Python (Cheat Sheet)

Machine learning methods can be used for classification and forecasting on time series problems. Before exploring machine learning methods for time series, it is a good idea to ensure you have exhausted classical linear time series forecasting methods. Classical time series forecasting methods may be focused on linear relationships, nevertheless, they are sophisticated and perform …

Eric Jang: Dijkstra's in Disguise

You can find a PDF version of this blog post here . A weighted graph is a data structure consisting of some vertices and edges, and each...

Why decentralized social networking never makes it — ever heard of Crossing the Chasm? – Upon 2020

Every now and then, the “why hasn’t decentralized social networking succeeded” discussion pops back up. And inevitably, that motivates somebody who thinks they can do better. They proceed to design a new set of decentralized networking protocols, write lots of code, and get early adopters to enthusiastically adopt the New Thing. Which then, inevitably, never grows beyond a certain size.

Rinse and repeat.

How many times has that now happened? And keeps happening?

Has anybody considered that perhaps the protocols weren’t the problem? Or whether the code was written in one language or another, or did or didn’t use HTML5 or other cool new tech?