>>> class A:
... x = 2
...
>>> A.x
2
>>> A().x
2
It’s time to talk about exceptions or, rather, exceptional situations. Before we start, let’s look at the definition. What is an exceptional situation?
This is a situation that makes the execution of current or subsequent code incorrect. I mean different from how it was designed or intended. Such a situation compromises the integrity of an application or its part, e.g. an object. It brings the application into an extraordinary or exceptional state.
But why do we need to define this terminology? Because it will keep us in some boundaries. If we don’t follow the terminology, we can get too far from a designed concept which may result in many ambiguous situations. Let’s see some practical examples:
struct Number
{
public static Number Parse(string source)
{
// ...
if(!parsed)
{
throw new ParsingException();
}
// ...
}
public static bool TryParse(string source, out Number result)
{
// ..
return parsed;
}
}This example seems a little strange, and it is for a reason. I made this code slightly artificial to show the importance of problems appearing in it. First, let’s look at the Parse method. Why should it throw an exception?
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:
