Robik Shrestha erobic

## README.md

      
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                steveharoz
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                — forked from cjrd/README.md
            
              
                Interactive tool for creating directed graphs using d3.js.
              
          
    directed-graph-creator

Interactive tool for creating directed graphs, created using d3.js.
Operation:

drag/scroll to translate/zoom the graph
shift-click on graph to create a node
shift-click on a node and then drag to another node to connect them with a directed edge


## resnet_all_conv.py
"""
Possibly correct implementation of an all conv neural network using a single residual module

This code was written for instruction purposes and no attempt to get the best results were made.

References:
Deep Residual Learning for Image Recognition: http://arxiv.org/pdf/1512.03385v1.pdf
STRIVING FOR SIMPLICITY, THE ALL CONVOLUTIONAL NET: http://arxiv.org/pdf/1412.6806v3.pdf

A video walking through the code and main ideas: https://youtu.be/-N_zlfKo4Ec

## load_jpeg_with_tensorflow.py
# Typical setup to include TensorFlow.
import tensorflow as tf

# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("./images/*.jpg"))

# Read an entire image file which is required since they're JPEGs, if the images
# are too large they could be split in advance to smaller files or use the Fixed

## char-rnn recipes.md

      
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                nylki
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              November 22, 2025 10:34
            
              
                char-rnn cooking recipes
              
          
    do androids dream of cooking?

The following recipes are sampled from a trained neural net.
You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn
Thanks to  Andrej Karpathy for the great code! It's really easy to setup.
The recipes I used for training the char-rnn are from a recipe collection called ffts.com
And here is the actual zipped data (uncompressed ~35 MB) I used for training.
The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.

  
## fisher_vector.py
import numpy as np
import pdb

from sklearn.datasets import make_classification
from sklearn.mixture import GaussianMixture as GMM


def fisher_vector(xx, gmm):
    """Computes the Fisher vector on a set of descriptors.
	"""
	Possibly correct implementation of an all conv neural network using a single residual module

	This code was written for instruction purposes and no attempt to get the best results were made.

	References:
	Deep Residual Learning for Image Recognition: http://arxiv.org/pdf/1512.03385v1.pdf
	STRIVING FOR SIMPLICITY, THE ALL CONVOLUTIONAL NET: http://arxiv.org/pdf/1412.6806v3.pdf

	A video walking through the code and main ideas: https://youtu.be/-N_zlfKo4Ec
	# Typical setup to include TensorFlow.
	import tensorflow as tf

	# Make a queue of file names including all the JPEG images files in the relative
	# image directory.
	filename_queue = tf.train.string_input_producer(
	tf.train.match_filenames_once("./images/*.jpg"))

	# Read an entire image file which is required since they're JPEGs, if the images
	# are too large they could be split in advance to smaller files or use the Fixed
	import numpy as np
	import pdb

	from sklearn.datasets import make_classification
	from sklearn.mixture import GaussianMixture as GMM


	def fisher_vector(xx, gmm):
	"""Computes the Fisher vector on a set of descriptors.