Ahmed Ibrahim aicentral

## ml_simplified.ipynb

      
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                aicentral
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              January 1, 2023 13:57
            
              
                ML_simplified.ipynb
              
          
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## depth_using_midas.py
import torch
from torchvision.transforms import Compose, ToTensor, Resize
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import torch
import os
import numpy as np


## GradientDescent.py
# Simple implementation for the example in page 96 in http://www.deeplearningbook.org/contents/numerical.html
# 4.5 Example: Linear Least Squares
import numpy as np
X=np.random.rand(2,1)
sigma=0.000001
epslon=0.03
A=np.array([[1,2],[3,4]])
B=np.array([1,1])
def gradient(A,X,B):
    return np.dot(np.dot(np.transpose(A),A),X)-np.dot(np.transpose(A),B)

## cifar10_allcnn.py
#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright 2015 Nervana Systems Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
	import torch
	from torchvision.transforms import Compose, ToTensor, Resize
	from torchvision.datasets import ImageFolder
	from torch.utils.data import DataLoader
	import matplotlib.pyplot as plt
	import torch
	import os
	import numpy as np
	# Simple implementation for the example in page 96 in http://www.deeplearningbook.org/contents/numerical.html
	# 4.5 Example: Linear Least Squares
	import numpy as np
	X=np.random.rand(2,1)
	sigma=0.000001
	epslon=0.03
	A=np.array([[1,2],[3,4]])
	B=np.array([1,1])
	def gradient(A,X,B):
	return np.dot(np.dot(np.transpose(A),A),X)-np.dot(np.transpose(A),B)
	#!/usr/bin/env python
	# ----------------------------------------------------------------------------
	# Copyright 2015 Nervana Systems Inc.
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software