We have time-series data with daily and weekly regularity. We want to ﬁnd the way how to model this data in an optimal way.
Keeping your code consistent and well formatted not an easy task even when you work alone. But when you work with a team or with open source project all start getting even harder. Everyone has own code style, someone doesn’t run tests, and no one writes documentation. This article will help you to set up all these things and even more — automate this routine to never do it manually.
After reading you will get your own npm-ready project with next features:
I've found some difficulties while using it in my project. Here they are:
sticky-changeevent relates to header element, but you have to insert sentinels to header's parent (and make it
40px, which is top-sentinel's height.
Let's try to improve it!
ctrl+arrowsto move between words, it is easier to press Esc, e and then go back to the
iediting mode. Understandably, all this trouble because the author finds it inconvenient to hold
Our project implements a real-time edge detection system based on capturing image frames from an OV7670 camera and streaming them to a VGA monitor after applying a grayscale filter and Sobel operator. Our design is built on a Cyclone IV FPGA board which enables us to optimize the performance using the powerful features of the low-level hardware and parallel computations which is important to meet the requirements of the real-time system.
We used ZEOWAA FPGA development board which is based on Cyclone IV (EP4CE6E22C8N). Also, we used Quartus Prime Lite Edition as a development environment and Verilog HDL as a programming language. In addition, we used the built-in VGA interface to drive the VGA monitor, and GPIO (General Pins for Input and Output) to connect the external hardware with our board.
When you study an abstract subject like linear algebra, you may wonder: why do you need all these vectors and matrices? How are you going to apply all this inversions, transpositions, eigenvector and eigenvalues for practical purposes?
Well, if you study linear algebra with the purpose of doing machine learning, this is the answer for you.
In brief, you can use linear algebra for machine learning on 3 different levels: