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## seedling-resnet101-Copy1.ipynb

      
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                asvcode
                / seedling-resnet101-Copy1.ipynb
            
            
              Created
              December 29, 2017 06:53
                — forked from anonymous/seedling-resnet101-Copy1.ipynb
            
              
                fastai/courses/dl1/seedling-resnet101-Copy1.ipynb
              
          
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## nasnet.py
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.autograd import Variable

pretrained_settings = {
    'nasnetalarge': {
        'imagenet': {
            'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',

## Occupancy_Using_Fastai.ipynb

      
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                asvcode
                / Occupancy_Using_Fastai.ipynb
            
            
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              January 24, 2019 09:22
            
          
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## Occupancy_Using_Regular_Techniques.ipynb

      
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                asvcode
                / Occupancy_Using_Regular_Techniques.ipynb
            
            
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              January 24, 2019 17:31
            
          
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## requirements.txt
absl-py==0.6.1
asn1crypto==0.24.0
astor==0.7.1
attrs==18.2.0
backcall==0.1.0
bcolz==1.2.1
beautifulsoup4==4.6.3
bleach==3.0.2
bokeh==1.0.1
boto==2.49.0

## 001Thyroid_Quick_and_Dirty_EDA.ipynb

      
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                asvcode
                / 001Thyroid_Quick_and_Dirty_EDA.ipynb
            
            
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              May 3, 2020 01:56
            
          
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## 001Thyroid_Quick_and_Dirty_EDA.ipynb

      
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              May 3, 2020 01:57
            
          
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## 001Thyroid_Quick_and_Dirty_EDA.ipynb

      
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              May 3, 2020 01:59
            
          
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## scale_down_image
scaled = scaled_px(patient6)
crop = scaled[280:400,300:450]
show_image(crop, cmap='bone');

## fastai_dependancies
from fastai.vision.all import *
from fastai.medical.imaging import *

import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (7,7)

source = 'D:/Datasets/rsna_pulmonary'
files = get_dicom_files(source)
patient6 = files[6].dcmread()
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torch.utils.model_zoo as model_zoo
	from torch.autograd import Variable

	pretrained_settings = {
	'nasnetalarge': {
	'imagenet': {
	'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
	absl-py==0.6.1
	asn1crypto==0.24.0
	astor==0.7.1
	attrs==18.2.0
	backcall==0.1.0
	bcolz==1.2.1
	beautifulsoup4==4.6.3
	bleach==3.0.2
	bokeh==1.0.1
	boto==2.49.0
	scaled = scaled_px(patient6)
	crop = scaled[280:400,300:450]
	show_image(crop, cmap='bone');
	from fastai.vision.all import *
	from fastai.medical.imaging import *

	import matplotlib.pyplot as plt
	plt.rcParams["figure.figsize"] = (7,7)

	source = 'D:/Datasets/rsna_pulmonary'
	files = get_dicom_files(source)
	patient6 = files[6].dcmread()