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About learning python and finding information
A lot of python content is covered in this tutorial, but there are still a lot of content not covered, if you need to learn, then please use the following method.
Beginners may have a lot of questions. Don’t be afraid. Ask someone who knows it. It’s a good choice to make good use of Google.
If you are a programmer, you must have heard of stackoverflow, then it is a good choice to go to them to ask questions or see other people's answers.
Sometimes reddit will give you some new ideas, you can join sub to see how others program.
The video site YouTube can teach you some things. Many things cannot be explained clearly through screenshots, so watching videos is a good choice.
Pypi contains almost all packages of python, if you have questions about any package, you should check here.
The rookie tutorial is one of the best Chinese tutorials
w3schools is a dictionary type tutorial, which is not suitable for beginners, but if you have any functions that you don’t know, you can look it up in it.
Python's drawing library is one of the most commonly used libraries, you can learn from it here.
numpy is one of the most commonly used math libraries. You can find numpy related documents here.
Sklearn is one of the most commonly used machine learning libraries. If you have any questions, you should consult the official sklearn documentation.
It is worth noting that machine learning does not support large-scale data. If your data exceeds a certain size, then machine learning may not be able to solve it (it takes a few hours to run, or needs to use more than 100GB of memory), At this time you need to use a deep learning framework.
Keras is a very easy to use deep learning re-encapsulation framework, if you have any questions, then you should check here.
Keras is a secondary encapsulation of tensorflow, so if you have more in-depth questions, you should ask tensorflow
Pytorch is also our very important deep learning library. Keras also supports packaging with pytorch as the bottom layer, so if you are considering using pytorch then you should take a look up in pytorch.
When you are doing visualization, are you confused about not knowing what chart to use, or you don’t know how to display the data?
Then it's a good idea to take a look at the gallery for inspiration.
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Start time of this page: December 20, 2021
Completion time of this page: December 20, 2021
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