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## pytorch_example.ipynb

      
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## jax_gan.ipynb

      
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## trajectory_forecasting_handson.ipynb

      
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## jax_cnn_on_cifar10.ipynb

      
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                / jax_cnn_on_cifar10.ipynb
            
            
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              October 2, 2021 11:44
            
              
                jax_CNN_on_CIFAR10.ipynb
              
          
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## jax_mlp_on_mnist.ipynb

      
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              October 5, 2021 06:24
            
              
                jax_MLP_on_MNIST.ipynb
              
          
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## mnist_train_6000_samples.py
from torchvision import datasets, transforms
import torchvision.models as models
from torch.utils.data import DataLoader, Subset

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F


## fl_pytorch.ipynb

      
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              April 7, 2021 01:15
            
              
                fl_pytorch.ipynb
              
          
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## gflow.py
import cv2
import numpy as np
from glob import glob
import sys


def main(dirname, ext='.jpg', step=5, vis=False):
    # Estimating global motion by 1) estimating dense optical flows, 2) estimating homography matrix,
    # and 3) calculating global motion flows based on the homography.
    # gm_flow will store four global motion at four corners ([25 | 75]-percentile points of [width | height]).

## atom-sync-settings
atom setting
	from torchvision import datasets, transforms
	import torchvision.models as models
	from torch.utils.data import DataLoader, Subset

	import torch
	import torch.nn as nn
	import torch.optim as optim
	import torch.nn.functional as F
	import cv2
	import numpy as np
	from glob import glob
	import sys


	def main(dirname, ext='.jpg', step=5, vis=False):
	# Estimating global motion by 1) estimating dense optical flows, 2) estimating homography matrix,
	# and 3) calculating global motion flows based on the homography.
	# gm_flow will store four global motion at four corners ([25 \| 75]-percentile points of [width \| height]).