PankajMehar PankajMehar

## it-ebooks.md

      
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                PankajMehar
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              Created
              November 9, 2020 07:51
                — forked from baiwfg2/it-ebooks.md
            
              
                Download ebooks as you want
              
          
    The initial source comes from sdcuike/issueBlog#4

http://greenteapress.com/wp/
https://papers.nips.cc
https://github.com/PacktPublishing  free to download books code by Packet
https://love2.io Chinese site

  
## tensorflow-gpu-ubuntu.md

      
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                PankajMehar
                / tensorflow-gpu-ubuntu.md
            
            
              Created
              October 26, 2018 19:16
                — forked from ljaraque/tensorflow-gpu-ubuntu.md
            
              
                Install tensorflow-gpu in ubuntu
              
          
    Install tensorflow-gpu1.8 in ubuntu18.04 with CUDA9.2, cuDNN7.2.1 and NVIDIA Driver 396

ljaraque@yahoo.com
Overview

This is a summary of the process I lived in order to enable my system with CUDA9.2, cuDNN7.2.1, Tensorflow1.8 and NVIDIA GEFORCE GTX860M GPU. You can just skip the steps marked with FAILED. I decided to keep them there in order to be useful  for others who tried those paths too.
FAILED (Next section is successfull) Install NVIDIA driver (FAILED, THIS WILL INSTALL DRIVER 390 which is not compatible with CUDA9.2):

ubuntu-drivers devices


## 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)

## CoNLL-2003.md

      
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                PankajMehar
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              Created
              May 26, 2018 11:47
            
              
                CoNLL-2003
              
          
    CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
The CoNLL-2003 (Sang et al. 2003) shared task deals with language-independent named entity recognition as well (English and German).
Dataset

The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on a separate line and there is an empty line after each sentence. The first item on each line is a word, the second a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase.
The English data is a collection of news wire articles from the Reuters Corpus. The annotation has been done

  
## proportions_of_missing_data_in_dataframe_columns.py
# Author: Luke Schoen 2017
import pandas as pd
import numpy as np
import functools

# Create DataFrame
# df = pd.DataFrame(np.random.randn(10,2))

# Populate with NaN values
df = pd.DataFrame({'col1': ['1.111', '2.111', '3.111', '4.111'], 'col2': ['4.111', '5.111', np.NaN, '7.111'], 'col3': ['8', '9', np.NaN, np.NaN], 'col4': ['12', '13', '14', '15']})

## useful_pandas_snippets.py
# List unique values in a DataFrame column
# h/t @makmanalp for the updated syntax!
df['Column Name'].unique()

# Convert Series datatype to numeric (will error if column has non-numeric values)
# h/t @makmanalp
pd.to_numeric(df['Column Name'])

# Convert Series datatype to numeric, changing non-numeric values to NaN
# h/t @makmanalp for the updated syntax!
	"""
	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)
	# Author: Luke Schoen 2017
	import pandas as pd
	import numpy as np
	import functools

	# Create DataFrame
	# df = pd.DataFrame(np.random.randn(10,2))

	# Populate with NaN values
	df = pd.DataFrame({'col1': ['1.111', '2.111', '3.111', '4.111'], 'col2': ['4.111', '5.111', np.NaN, '7.111'], 'col3': ['8', '9', np.NaN, np.NaN], 'col4': ['12', '13', '14', '15']})
	# List unique values in a DataFrame column
	# h/t @makmanalp for the updated syntax!
	df['Column Name'].unique()

	# Convert Series datatype to numeric (will error if column has non-numeric values)
	# h/t @makmanalp
	pd.to_numeric(df['Column Name'])

	# Convert Series datatype to numeric, changing non-numeric values to NaN
	# h/t @makmanalp for the updated syntax!