Aditya Kaushik 97k

## Inference for Seq2Seq Models.ipynb

      
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                bentrevett
                / Inference for Seq2Seq Models.ipynb
            
            
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## data_loader.py
"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

## multilabel_example.py
import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
from torch.autograd import Variable

# (1, 0) => target labels 0+2
# (0, 1) => target labels 1
# (1, 1) => target labels 3
train = []

## README.md

      
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                guillaumevincent
                / README.md
            
            
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                Windows Service with Python 3.5 and pyinstaller
              
          
    TestService

Windows Service with Python 3.5 and pyinstaller
Requirements


Python 3.5.x
Visual C++ Build Tools 2015
PyInstaller 3.2


## 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)

## README-Template.md

      
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                PurpleBooth
                / README-Template.md
            
            
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                A template to make good README.md
              
          
    Project Title

One Paragraph of project description goes here
Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Prerequisites


## octave.md

      
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                obstschale
                / octave.md
            
            
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                An Octave introduction cheat sheet.
              
          
    Octave CheatSheet


GNU Octave is a high-level interpreted language, primarily intended for numerical computations.

(via GNU Octave)


Hint: I also mad an octave docset for Dash: https://github.com/obstschale/octave-docset

Basics
	"""
	Create train, valid, test iterators for CIFAR-10 [1].
	Easily extended to MNIST, CIFAR-100 and Imagenet.

	[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
	"""

	import torch
	import numpy as np
	import torch
	import torch.nn as nn
	import numpy as np
	import torch.optim as optim
	from torch.autograd import Variable

	# (1, 0) => target labels 0+2
	# (0, 1) => target labels 1
	# (1, 1) => target labels 3
	train = []
	"""
	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)