MoLiang AutuanLiu

## tmux-cheatsheet.markdown

      
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              April 28, 2019 10:10
                — forked from ryerh/tmux-cheatsheet.markdown
            
              
                Tmux 快捷键 & 速查表
              
          
    注意：本文内容适用于 Tmux 2.3 及以上的版本，但是绝大部分的特性低版本也都适用，鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。
Tmux 快捷键 & 速查表

启动新会话：
tmux [new -s 会话名 -n 窗口名]

恢复会话：

  
## conv.sh
curl -O https://gist.githubusercontent.com/akimach/27e87cb7f97dc253921b9ea8b7b332b5/raw/fd112a66a3ca953dd7ad26098cccfe1d532ba0d7/binominal_confidience_interval.ipynb
jupyter-nbconvert binominal_confidience_interval.ipynb --to markdown
pandoc binominal_confidience_interval.ipynb -t docx -o binominal_confidience_interval.docx

## __init__.pyi

from typing import List, Tuple, Optional, Union, Any, ContextManager, Callable, overload

import builtins
import math
import pickle


class dtype: ...
_dtype = dtype

## 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)

## 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
	curl -O https://gist.githubusercontent.com/akimach/27e87cb7f97dc253921b9ea8b7b332b5/raw/fd112a66a3ca953dd7ad26098cccfe1d532ba0d7/binominal_confidience_interval.ipynb
	jupyter-nbconvert binominal_confidience_interval.ipynb --to markdown
	pandoc binominal_confidience_interval.ipynb -t docx -o binominal_confidience_interval.docx

	from typing import List, Tuple, Optional, Union, Any, ContextManager, Callable, overload

	import builtins
	import math
	import pickle


	class dtype: ...
	_dtype = dtype
	"""
	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)
	"""
	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