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Python | Machine Learning | Data Science WhatsApp Channel. Welcome to our official WhatsApp Channel โ your daily dose of AI, Python, and cutting-edge technology!
Here, we share:
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AI & machine learning tipsโฆ
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Python tutorials and ready-to-use code snippets
AI & machine learning tipsโฆ
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---
๐ Scenario:
Imagine you have a variable in a class and want to access it in a method. For example:
class MyClass:
my_variable = "I am a class variable"
def my_method(self):
return f"Accessing variable: {self.my_variable}"
# Test
obj = MyClass()
print(obj.my_method())
---
๐ Explanation:
- In this example,
my_method
is a regular instance method with the self
argument. - You can access the class variable with
self.my_variable
, but you need to create an instance of the class (obj = MyClass()
). - What if you want to access it without creating an instance? Thatโs where
@classmethod
comes in! ---
If you want to access the variable directly using the class name, use
@classmethod
:class MyClass:
my_variable = "I am a class variable"
@classmethod
def my_method(cls):
return f"Accessing variable: {cls.my_variable}"
# Test
print(MyClass.my_method())
---
- In the first case (regular method), you need to create an instance to call the method.
- In the second case (with
@classmethod
), you can call the method directly with the class name (
MyClass.my_method()) and
cls gives you access to class variables. - Another option is
@staticmethod
, but youโd have to manually write the class name (e.g., MyClass.my_variable
).---
- If you want to work with an instance โก๏ธ Use a regular method with
self
. - If you want to work directly with the class โก๏ธ Use
@classmethod
. Which method do you like more? Drop your thoughts in the comments! ๐ฃ๏ธ
#Python #ProgrammingTips #PythonClass
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Python Data Science Jobs & Interviews
Your go-to hub for Python and Data Scienceโfeaturing questions, answers, quizzes, and interview tips to sharpen your skills and boost your career in the data-driven world.
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What will this Python code output? #junior #python
A task for beginners.
A task for beginners.
s = set()
a = [1, 2, 3]
s.add(tuple(a))
print(s)
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Article Title:
EasyVolcap: Accelerating Neural Volumetric Video Research
Article Date: 11 Dec 2023
Article Description:
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations. When acquired, such volumography can be viewed from any viewpoint and timestamp on flat screens, 3D displays, or VR headsets, enabling immersive viewing experiences and more flexible content creation in a variety of applications such as sports broadcasting, video conferencing, gaming, and movie productions. With the recent advances and fast-growing interest in neural scene representations for volumetric video, there is an urgent need for a unified open-source library to streamline the process of volumetric video capturing, reconstruction, and rendering for both researchers and non-professional users to develop various algorithms and applications of this emerging technology. In this paper, we present EasyVolcap, a Python & Pytorch library for accelerating neural volumetric video research with the goal of unifying the process of multi-view data processing, 4D scene reconstruction, and efficient dynamic volumetric video rendering. Our source code is available at https://github.com/zju3dv/EasyVolcap.PDFAbstract
PDF Download Link:
https://arxiv.org/pdf/2312.06575v1.pdf
GitHub:
โข https://github.com/zju3dv/easyvolcap
Datasets:
โข NeRF
==================================
For more data science resources:
โ https://t.iss.one/DataScienceT
EasyVolcap: Accelerating Neural Volumetric Video Research
Article Date: 11 Dec 2023
Article Description:
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations. When acquired, such volumography can be viewed from any viewpoint and timestamp on flat screens, 3D displays, or VR headsets, enabling immersive viewing experiences and more flexible content creation in a variety of applications such as sports broadcasting, video conferencing, gaming, and movie productions. With the recent advances and fast-growing interest in neural scene representations for volumetric video, there is an urgent need for a unified open-source library to streamline the process of volumetric video capturing, reconstruction, and rendering for both researchers and non-professional users to develop various algorithms and applications of this emerging technology. In this paper, we present EasyVolcap, a Python & Pytorch library for accelerating neural volumetric video research with the goal of unifying the process of multi-view data processing, 4D scene reconstruction, and efficient dynamic volumetric video rendering. Our source code is available at https://github.com/zju3dv/EasyVolcap.PDFAbstract
PDF Download Link:
https://arxiv.org/pdf/2312.06575v1.pdf
GitHub:
โข https://github.com/zju3dv/easyvolcap
Datasets:
โข NeRF
==================================
For more data science resources:
โ https://t.iss.one/DataScienceT
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