CV代做 | oop代做 | Python | unity | Computer Vision | assignment | lab作业 – Computer Vision

Computer Vision

CV代做 | oop代做 | Python | unity | Computer Vision | assignment | lab作业 – 该题目是一个常规的Computer Vision的练习题目代写, 是有一定代表意义的c++/oop/Python/Computer Vision等代写方向, 这个项目是lab代写的代写题目

html代写 代写html 网站代写 网页代写 Coding environment: Computer Vision

Coding environment

For all of the coding tasks in the course it will be convenient -- and in some cases required -- that
you use  Python for your coding and use this within a notebook.
Python
If you are not familiar with python, do not be alarmed. This course is a great opport unity to get
some familiarity. Python is one of the most popular languages for doing work in various forms of
machine learning, including computer vision. It is dynamically typed and interpreted, both of which
make it easier to use than other  oop languages such as  c++ or Java, though you will find
support for all the usual programmning constructs. It also has a very large number of modules
and libraries for doing some amazing things meaning you can rapidly build quite advanced ideas
into your code.
Jupyter Notebooks
Although python is a stand-alone language, a great cross-platform way to use it is
through notebooks and a corresponding web-interface. A jupyter notebook is a json file that
contains text, code and even code output, stored with the extension .ipynb. You can load it into a
suitable web-based IDE and run the code, step through it cell by cell, edit it and so forth.
There are two web-interfaces recommended for this course:
  1. Jupyter (or jupyter-lab ) is a python application that understands jupyter notebooks. To use jupyter, you install it on your local machine and run a jupyter (or jupyter-lab) server. You then point your browser at a local URL (that the server tells you) and view the IDE through the browser. The IDE allows you to load a notebook and edit, run, or step through the python code. You can even save a notebook with results and convert to pdf to submit for assignment work. Some high-level instructions for getting your own machine set up to run jupyter are provided below.
  2. Google colabs , like jupyter, provides a web-based IDE that can load and run jupyter notebooks. The difference is that instead of looking at your local filestore, and running python locally, colabs views the file-system on your google drive, and runs all the code on a virtual machine in the cloud. All of the python modules and libraries needed for this course are already loaded into the default co lab environment so there is almost no setup required. You just need a google drive for cloud storage and colabs will let you step through code, execute it, edit it, etc

Setting up your own machine

A brief video (https://myuni.adelaide.edu.au/media_objects_iframe/m-
3ZFVQ2gikjAJRgXDJtbpELxUxQFPyy84?type=video?type=video) on setting up google colab
A page (https://myuni.adelaide.edu.au/courses/82199/pages/jupyter-and-anaconda) about setting
up jupyter

2023/3/8 21:53 Coding environment: Computer Vision

https://myuni.adelaide.edu.au/courses/82199/pages/coding-environment 2 / 2

Resources

The following links (to external sites) provide further tutorial instruction in python, colabs and
scikit, an important python library
https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-
colab.ipynbLinks
(https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-
colab.ipynbLinks) to an external site. (Strongly recommended!)
https://docs.python.org/3/tutorial/ (https://docs.python.org/3/tutorial/Links)
https://scikit-learn.org/stable/tutorial/index. html (https://scikit-
learn.org/stable/tutorial/index.html)