Angel G. AGenchev

## orthodoxc++.md

      
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                bkaradzic
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              April 23, 2024 13:59
            
              
                Orthodox C++
              
          
    Orthodox C++

What is Orthodox C++?

Orthodox C++ (sometimes referred as C+) is minimal subset of C++ that improves C, but avoids all unnecessary things from so called Modern C++. It's exactly opposite of what Modern C++ suppose to be.
Why not Modern C++?


## mount_vbox_shared_boot.md

      
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                kentwait
                / mount_vbox_shared_boot.md
            
            
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              November 14, 2023 11:22
            
              
                Mount VirtualBox shared folder on boot using fstab
              
          
    How to mount a VirtualBox shared folder when the Guest OS boots

Problem

While using VirtualBox's Guest Additions to mount shared folders provides a seamless way to mount shared folders, there are also disadvantages.

Shared folders will always mount in /media/sf_(share name) unless specified using through vbox drivers in the guest OS
Mounting does not happen at boot-time. Applications that require paths at boot will not be able to access the shared folder even after it is mounted - such as Docker.

Solution

Instead of relying on Guest Additions, if you have sudo permissions, shared folders can be mounted at boot-time using fstab. Using this approach allows

  
## fitting_straight_line_pytorch___09___train_model.py
losses = []         # to keep track of the epoch lossese
slope_list = []     # to keep track of the slope learnt by the model
intercept_list = [] # to keep track of the intercept learnt by the model

EPOCHS = 2500
print('\nTRAINING...')
for epoch in range(EPOCHS):
    # We need to clear the gradients of the optimizer before running the back-propagation in PyTorch
    optimizer.zero_grad()
	losses = [] # to keep track of the epoch lossese
	slope_list = [] # to keep track of the slope learnt by the model
	intercept_list = [] # to keep track of the intercept learnt by the model

	EPOCHS = 2500
	print('\nTRAINING...')
	for epoch in range(EPOCHS):
	# We need to clear the gradients of the optimizer before running the back-propagation in PyTorch
	optimizer.zero_grad()