Computing for the Sciences Using Python, Part 1

Introduction to Data Science: How to “Big Data” with Python
Free download. Book file PDF easily for everyone and every device. You can download and read online Computing for the Sciences Using Python, Part 1 file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Computing for the Sciences Using Python, Part 1 book. Happy reading Computing for the Sciences Using Python, Part 1 Bookeveryone. Download file Free Book PDF Computing for the Sciences Using Python, Part 1 at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Computing for the Sciences Using Python, Part 1 Pocket Guide. I will be covering the following topics in this Python functions article:. Functions manage the inputs and outputs in computer programs. Programming languages are designed to work on data and functions are an effective way to manage and transform this data. The modifications are generally done to drive outcomes like performing tasks and finding results. And, the set of operations or instructions required to do so comes from logically functional blocks of code that can be reused independently from the main program.

In fact, the main code is also a function, just a very important one. Every other function is logically aligned and maintained to functionally execute from your main code.

Today I Finished MITx 600 Part 1!

But, if the function has not been defined, you'll just have to define one yourself before using it. This is because the definition lists the steps of its operation. So, functions are nothing but tasks that a user wants to perform. But, defining it once with a name will let you reuse that functionality without making your main programs look too scary.

This drastically reduces lines of code and even makes debugging easier. We'll be getting into that shortly, but the first reason behind why we use functions is because of their reusability. The fact that even complex operations could be put together as singular tasks that would run with just a call by its name is what has made code today so much clearer as well. Every programming language lets you create and use these functions to perform various tasks with just a call.

And, you could call it any number of times without having to worry about logically structuring its code into your main code every single time. Say, you have a television that stores many channels on it, receives their digital radio broadcasts, converts them into what we watch, while also giving us additional options for a variety of other features as well.

But that doesn't mean there is someone logically scripting the lines of codes for what you watch every time you turn on your tv or flip a channel. Rather, functions for each task have been logically defined once and keep getting reused time and again according to the features you try to use. All of this happens by calling different functions as many times as needed from the main function that is running.

So, even if you're turning the volume up or down, its defined function is being called repeatedly. And, having a system operating the main code to keep calling these functions as and when needed has also made designing and innovating upon it easier. The important thing to note is that whenever this function is called, it executes its tasks depending on the instructions specified in it.

That's why machines can have different functions. A calculator is probably the most common example of this. It has provisions for addition, subtraction, multiplication, division, and other functions. All its functions have been clearly predefined into it, but it only performs those that you choose to call by pressing a respective button. Programmers reduce coding time and debugging time, thereby reducing overall development time, by using functions. Next up on this Python functions post, let's look at what Python functions actually are.

Now, this filter is also an array of numbers where the numbers are called weights or parameters. A very important note is that the depth of this filter has to be the same as the depth of the input, so the dimensions of this filter are 3 x 3 x 3. These multiplications are all summed up. So now we have a single number.

Week 0 - Statements, expressions, variables

Remember, this number is just representative of when the filter is at the top left of the image. Now, we repeat this process for every location on the input volume. Every unique location on the input volume produces a number. We can also choose stride or the step size 2 or more, but we have to care whether it will fit or not on the input image. The reason we get a 30 x 30 array is that there are different locations that a 3 x 3 filter can fit on a 32 x 32 input image. These numbers are mapped to a 30 x 30 array.

We can calculate the convolved image by following:. So, in this case, the output would be.

Python Tutorial for Beginners [Full Course] 2019

Moreover, we practically use more filters instead of one. However, For the pixels on the border of the image matrix, some elements of the kernel might stand out of the image matrix and therefore does not have any corresponding element from the image matrix. Now, I do realize that some of these topics are quite complex and could be made in whole posts by themselves. In an effort to remain concise yet retain comprehensiveness, I will provide links to resources where the topic is explained in more detail. This has the effect of burning the image, by averaging each pixel with those nearby:.

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