Changed the deep learning framework from tensorflow to pytorch

[shravya312]

#18

[Devanik21]

@shravya312 , interesting, but can we be benefited from it ?

[Devanik21]

can we add more features to our model ?

[shravya312]

A dynamic computation graph is a key feature of PyTorch that allows the framework to create a map of calculations while the program is running.

Key Points:
Built While Running: The graph is formed as you run your code, not before. This means it can change every time you execute it.

Flexible and Adaptive: You can easily change how your model behaves based on the data you provide. For example, if the input data is different, the graph adjusts accordingly.

Easy to Debug: Since the graph is created as the code runs, you can use regular Python tools to check for errors right where they happen, making it easier to fix problems. these are the features of pytorch which is beneficial

[shravya312]

can we add more features to our model ?

Educational Resources
Learning Modules: Offer tutorials or modules that educate users on space mining, including best practices and case studies.
Resource Library: Create a library of articles, research papers, and case studies related to space mining and exploration.

[shravya312]

can we add more features to our model ?

Educational Resources Learning Modules: Offer tutorials or modules that educate users on space mining, including best practices and case studies. Resource Library: Create a library of articles, research papers, and case studies related to space mining and exploration.

this can be done using deep-learning which can use the mining data and results from this model to educate people

[shravya312]

Can u review @Devanik21 if any changes needed I will do