Tarun Kumar tarunk04

## generate_solved_sudoku.py
    def __generator__(self, grid, i, j):
        number = [1, 2, 3, 4, 5, 6, 7, 8, 9]
        if j > 8:
            j = 0
            i += 1
            # end condition
            if i > 8:
                return True

        # skipping cell if the cell has already a number

## solve_sudoku.py
    def __solve_backtrack__(self, i, j, visualize=False):
        # Changing row if col index reaches end of the row
        if j > 8:
            j = 0
            i += 1
            # end condition
            if i > 8:
                if visualize == False:
                    eel.draw_sudoku(self.sudoku_solved)
                return True

## validate.py
    def validate_cell(self, sudoku_solved, val, i, j):
        # getting index for block ( 9 blocks of 3x3 size)
        block_i = i // 3
        block_j = j // 3

        # check block
        for b_i in range(block_i * 3, block_i * 3 + 3):
            for b_j in range(block_j * 3, block_j * 3 + 3):
                if sudoku_solved[b_i][b_j] == val:
                    return False

## digit-recognition-tutorial-cnn-99-67-accuracy.ipynb

      
        
          
            
              
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                tarunk04
                / digit-recognition-tutorial-cnn-99-67-accuracy.ipynb
            
            
              Last active
              April 27, 2020 21:15
            
              
                MNIST data set tutorial. In this notebook, I have covered the necessary steps to approach any Machine Learning Classification Problem. Included Image Visualization for better understanding. Quick Links to the functions I have used to explore it in depth. Basic techniques such as Confusion Matrix, Image Augmentation, etc.
              
          
        
      
        
  
    
    

          
    
      
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	def __generator__(self, grid, i, j):
	number = [1, 2, 3, 4, 5, 6, 7, 8, 9]
	if j > 8:
	j = 0
	i += 1
	# end condition
	if i > 8:
	return True

	# skipping cell if the cell has already a number
	def __solve_backtrack__(self, i, j, visualize=False):
	# Changing row if col index reaches end of the row
	if j > 8:
	j = 0
	i += 1
	# end condition
	if i > 8:
	if visualize == False:
	eel.draw_sudoku(self.sudoku_solved)
	return True
	def validate_cell(self, sudoku_solved, val, i, j):
	# getting index for block ( 9 blocks of 3x3 size)
	block_i = i // 3
	block_j = j // 3

	# check block
	for b_i in range(block_i * 3, block_i * 3 + 3):
	for b_j in range(block_j * 3, block_j * 3 + 3):
	if sudoku_solved[b_i][b_j] == val:
	return False