Sarthak Sahu ssahu

## algorithms_openpose.md

      
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              25 stars
            
          
                alesolano
                / algorithms_openpose.md
            
            
              Last active
              August 26, 2021 19:47
            
              
                OpenPose TensorFlow Alogrithms
              
          
    All code is highly based on Ildoo Kim's code (https://github.com/ildoonet/tf-openpose) and derived from the OpenPose Library (https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/LICENSE)

  
## kfac_nano_eager_test.py
import numpy as np
import tensorflow as tf
import scipy
from tensorflow.contrib.eager.python import tfe
tfe.enable_eager_execution()

# manual numpy example
# X = np.array(([[0., 1], [2, 3]]))
# W0 = X
# W1 = np.array(([[0., 1], [2, 3]]))/10

## tensorflow_opencv_ubuntu_deps.sh.txt
#!/bin/bash


# Don't require you to constantly enter password for sudo:
sudo visudo
# In the bottom of the file, paste the following (without the `#`):
# paperspace ALL=(ALL) NOPASSWD: ALL
# Then press `ctl+o` then `enter` to save your changes, and `ctr+x` to exit nano

# Allow connection from your IP to any port- default seems to be just 22 (ssh)

## labels_1024.tsv
7
2
1
0
4
1
4
9
5
9

## tensorboard_logging.py
"""Simple example on how to log scalars and images to tensorboard without tensor ops.

License: BSD License 2.0
"""
__author__ = "Michael Gygli"

import tensorflow as tf
from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np

## classifier_from_little_data_script_3.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

## classifier_from_little_data_script_2.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

## classifier_from_little_data_script_1.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

## springer-free-maths-books.md

      
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              474 forks
            
          
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              2243 stars
            
          
                bishboria
                / springer-free-maths-books.md
            
            
              Last active
              April 25, 2024 06:27
            
              
                Springer made a bunch of books available for free, these were the direct links
              
          
    These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions
A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen
A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

  
## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
	import numpy as np
	import tensorflow as tf
	import scipy
	from tensorflow.contrib.eager.python import tfe
	tfe.enable_eager_execution()

	# manual numpy example
	# X = np.array(([[0., 1], [2, 3]]))
	# W0 = X
	# W1 = np.array(([[0., 1], [2, 3]]))/10
	#!/bin/bash


	# Don't require you to constantly enter password for sudo:
	sudo visudo
	# In the bottom of the file, paste the following (without the `#`):
	# paperspace ALL=(ALL) NOPASSWD: ALL
	# Then press `ctl+o` then `enter` to save your changes, and `ctr+x` to exit nano

	# Allow connection from your IP to any port- default seems to be just 22 (ssh)
	"""Simple example on how to log scalars and images to tensorboard without tensor ops.

	License: BSD License 2.0
	"""
	__author__ = "Michael Gygli"

	import tensorflow as tf
	from StringIO import StringIO
	import matplotlib.pyplot as plt
	import numpy as np
	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)