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Python - String Methods

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Strings - Methods

In addition to expression operators, string provides a set of method that implements more sophisticated text-processing methods.


Methods are functions that are associated with particular objects. Actually, they are attributes attached to objects. In general, expressions and built-in functions work across a range of types. But methods are specific to object types, in this case, strings.

The method call expression object.method(arguments) is evaluated from left to right. Python will fetch the method of the object and then call it passing in the arguments.


Following is the summary of the method.

We'll use a sample string:

s = 'Black holes are where God divided by zero'
s2 = 'sample.txt'
  1. capitalize( )
    Return a copy of the string with only its first character capitalized. For 8-bit strings, this method is locale-dependent.
    >>> s.capitalize()
    'Black holes are where god divided by zero'
    

  2. center( width[, fillchar])
    Return centered in a string of length width. Padding is done using the specified fillchar (default is a space).
    >>> s.center(50,'*')
    '****Black holes are where God divided by zero*****'
    

  3. count( sub[, start[, end]])
    Return the number of occurrences of substring sub in string S[start:end]. Optional arguments start and end are interpreted as in slice notation.
    >>> s.count('i')
    2
    >>> s.count('hole')
    1
    >>> map(s.count, s)
    [1, 2, 2, 1, 1, 7, 2, 3, 2, 6, 1, 7, 2, 3, 6, 7, 1, 2, 6, 3, 6, 7, 1, 3, 4, 7, 4, 2, 1, 2, 4, 6, 4, 7, 1, 1, 7, 1, 6, 3, 3]
    

  4. decode( [encoding[, errors]])
    Decodes the string using the codec registered for encoding. encoding defaults to the default string encoding. errors may be given to set a different error handling scheme. The default is 'strict', meaning that encoding errors raise UnicodeError. Other possible values are 'ignore', 'replace' and any other name registered via codecs.register_error.
    >>> s.decode()
    u'Black holes are where God divided by zero'
    

  5. encode( [encoding[,errors]])
    Return an encoded version of the string. Default encoding is the current default string encoding. errors may be given to set a different error handling scheme. The default for errors is 'strict', meaning that encoding errors raise a UnicodeError. Other possible values are 'ignore', 'replace', 'xmlcharrefreplace', 'backslashreplace'.

  6. endswith( suffix[, start[, end]])
    Return True if the string ends with the specified suffix, otherwise return False. suffix can also be a tuple of suffixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.
    >>> s.endswith('txt')
    False
    >>> s2.endswith('txt')
    True
    

  7. expandtabs( [tabsize])
    Return a copy of the string where all tab characters are expanded using spaces. If tabsize is not given, a tab size of 8 characters is assumed.



  8. find(sub[, start[, end]])
    Return the lowest index in the string where substring sub is found, such that sub is contained in the range [start, end]. Optional arguments start and end are interpreted as in slice notation. Return -1 if sub is not found.
    >>> S = 'aaaaaSPYbbbbSPYcccc'
    >>> where = S.find('SPY')
    >>> where
    5
    >>> S = S[:where] + 'SKY' + S[(where+3):]
    >>> S
    'aaaaaSKYbbbbSPYcccc'
    

  9. index( sub[, start[, end]])
    Like find(), but raise ValueError when the substring is not found.
    >>> s.index('where')
    16
    >>> s.index('one')
    Traceback (most recent call last):
      File "", line 1, in 
    ValueError: substring not found
    >>> s.find('one')
    -1
    

  10. isalnum( )
    Return true if all characters in the string are alphanumeric and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent.
    >>> 'abc123'.isalnum()
    True
    >>> 'abcdef'.isalnum()
    True
    >>> 'abc def'.isalnum()
    False
    

  11. isalpha( )
    Return true if all characters in the string are alphabetic and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent.
    >>> 'abc'.isalpha()
    True
    >>> 'abc7'.isalpha()
    False
    

  12. isdigit( )
    Return true if all characters in the string are digits and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent.
    >>> '123'.isdigit()
    True
    >>> '123a'.isdigit()
    False
    

  13. islower( )
    Return true if all cased characters in the string are lowercase and there is at least one cased character, false otherwise. For 8-bit strings, this method is locale-dependent.

  14. isspace( )
    Return true if there are only whitespace characters in the string and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent.
    >>> '1 f'.isspace()
    False
    >>> '  '.isspace()
    True
    

  15. istitle( )
    Return true if the string is a titlecased string and there is at least one character, for example uppercase characters may only follow uncased characters and lowercase characters only cased ones. Return false otherwise. For 8-bit strings, this method is locale-dependent.
    >>> 'Black holes'.istitle()
    False
    >>> 'Black Holes'.istitle()
    True
    

  16. isupper( )
    Return true if all cased characters in the string are uppercase and there is at least one cased character, false otherwise. For 8-bit strings, this method is locale-dependent.

  17. join( seq)
    Return a string which is the concatenation of the strings in the sequence seq. The separator between elements is the string providing this method.
    >>> seq = ['a','b','c','d']
    >>> print ''.join(seq)
    abcd
    >>> print '-'.join(seq)
    a-b-c-d
    >>> 
    >>> ''.join([`x` for x in xrange(101)])
    '0123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100'
    >>> 
    

  18. ljust( width[, fillchar])
    Return the string left justified in a string of length width. Padding is done using the specified fillchar (default is a space). The original string is returned if width is less than len(s).
    >>> '123'.ljust(10)
    '123       '
    

  19. lower( )
    Return a copy of the string converted to lowercase. For 8-bit strings, this method is locale-dependent.

  20. lstrip( [chars])
    Return a copy of the string with leading characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a prefix; rather, all combinations of its values are stripped:
    >>> '     too much left side space  '.lstrip()
    'too much left side space  '
    >>> 'www.bogotobogo.com'.lstrip('w.')
    'bogotobogo.com'
    



  21. partition( sep)
    Split the string at the first occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing the string itself, followed by two empty strings.
    >>> s.partition('are')
    ('Black holes ', 'are', ' where God divided by zero')
    

  22. replace( old, new[, count])
    Return a copy of the string with all occurrences of substring old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.
    >>> S = 'beetles'
    >>> S = S[:3] + 'xx' + S[5:]
    >>> S
    'beexxes'
    
    >>> S = 'beetles'
    >>> S = S.replace('ee','xx')
    >>> S
    'bxxtles'
    

  23. rfind( sub [,start [,end]])
    Return the highest index in the string where substring sub is found, such that sub is contained within s[start,end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure.
    >>> s
    'Black holes are where God divided by zero'
    >>> s.rfind(' ')
    36
    

  24. rindex( sub[, start[, end]])
    Like rfind() but raises ValueError when the substring sub is not found.

  25. rjust( width[, fillchar])
    Return the string right justified in a string of length width. Padding is done using the specified fillchar (default is a space). The original string is returned if width is less than len(s).
    >>> '123'.rjust(10)
    '       123'
    

  26. rpartition( sep)
    Split the string at the last occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing two empty strings, followed by the string itself.

  27. rsplit( [sep [,maxsplit]])
    Return a list of the words in the string, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done, the rightmost ones. If sep is not specified or None, any whitespace string is a separator. Except for splitting from the right, rsplit() behaves like split() which is described in detail below.

  28. rstrip( [chars])
    Return a copy of the string with trailing characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a suffix; rather, all combinations of its values are stripped:
    >>> ' too much right side space    '.rstrip()
    ' too much right side space'
    
    >>> 'mississippi'.rstrip('im')
    'mississipp'
    >>> 'mississippi'.rstrip('ip')
    'mississ'
    

  29. split( [sep [,maxsplit]])
    Return a list of the words in the string, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done. (thus, the list will have at most maxsplit+1 element).
    If maxsplit is not specified, then there is no limit on the number of splits (all possible splits are made). Consecutive delimiters are not grouped together and are deemed to delimit empty strings:
    >>> '1,,2'.split(',')
    ['1', '', '2']
    
    The sep argument may consist of multiple characters
    >>> '1, 2, 3'.split(', ')
    ['1', '2', '3']
    
    Splitting an empty string with a specified separator returns
    >>> ' '.split()
    []
    
    If sep is not specified or is None, a different splitting algorithm is applied. First, whitespace characters (spaces, tabs, newlines, returns, and formfeeds) are stripped from both ends. Then, words are separated by arbitrary length strings of whitespace characters. Consecutive whitespace delimiters are treated as a single delimiter
    >>> '1 2 3'.split()
    ['1', '2', '3']
    
    Splitting an empty string or a string consisting of just whitespace returns an empty list.

    To split a string with multiple delimiters, we can use re:

    import re
    # split with semicolon, comma, space
    >>> str = 'Life; a walking, shadow'
    >>> str1 = re.split(';|,| ', str)
    >>> str1
    ['Life', '', 'a', 'walking', '', 'shadow']
    >>>
    # split with semicolon+space, comma+space, space
    >>> str2 = re.split('; |, | ',str)
    >>> str2
    ['Life', 'a', 'walking', 'shadow']
      


  30. splitlines( [keepends])
    Return a list of the lines in the string, breaking at line boundaries. Line breaks are not included in the resulting list unless keepends is given and true.

  31. startswith( prefix[, start[, end]])
    Return True if string starts with the prefix, otherwise return False. prefix can also be a tuple of prefixes to look for. With optional start, test string beginning at that position. With optional end, stop comparing string at that position.

  32. strip( [chars])
    Return a copy of the string with the leading and trailing characters removed. The chars argument is a string specifying the set of characters to be removed.
    If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a prefix or suffix; rather, all combinations of its values are stripped:
    >>> '   too much space    '.strip()
    'too much space'
    >>> 'www.bogotobogo.com'.strip('.wmoc')
    'bogotobog'
    >>> 
    

  33. swapcase( )
    Return a copy of the string with uppercase characters converted to lowercase and vice versa. For 8-bit strings, this method is locale-dependent.

  34. title( )
    Return a titlecased version of the string: words start with uppercase characters, all remaining cased characters are lowercase. For 8-bit strings, this method is locale-dependent.

  35. translate( table[, deletechars])
    Return a copy of the string where all characters occurring in the optional argument deletechars are removed, and the remaining characters have been mapped through the given translation table, which must be a string of length 256.
    We can use the maketrans() helper function in the string module to create a translation table.
    For Unicode objects, the translate() method does not accept the optional deletechars argument. Instead, it returns a copy of the s where all characters have been mapped through the given translation table which must be a mapping of Unicode ordinals to Unicode ordinals, Unicode strings or None.
    Unmapped characters are left untouched.
    Characters mapped to None are deleted.

  36. upper( )
    Return a copy of the string converted to uppercase. For 8-bit strings, this method is locale-dependent.

  37. zfill(width)
    Return the numeric string left filled with zeros in a string of length width. The original string is returned if width is less than len(s).








  38. Python tutorial



    Python Home

    Introduction

    Running Python Programs (os, sys, import)

    Modules and IDLE (Import, Reload, exec)

    Object Types - Numbers, Strings, and None

    Strings - Escape Sequence, Raw String, and Slicing

    Strings - Methods

    Formatting Strings - expressions and method calls

    Files and os.path

    Traversing directories recursively

    Subprocess Module

    Regular Expressions with Python

    Regular Expressions Cheat Sheet

    Object Types - Lists

    Object Types - Dictionaries and Tuples

    Functions def, *args, **kargs

    Functions lambda

    Built-in Functions

    map, filter, and reduce

    Decorators

    List Comprehension

    Sets (union/intersection) and itertools - Jaccard coefficient and shingling to check plagiarism

    Hashing (Hash tables and hashlib)

    Dictionary Comprehension with zip

    The yield keyword

    Generator Functions and Expressions

    generator.send() method

    Iterators

    Classes and Instances (__init__, __call__, etc.)

    if__name__ == '__main__'

    argparse

    Exceptions

    @static method vs class method

    Private attributes and private methods

    bits, bytes, bitstring, and constBitStream

    json.dump(s) and json.load(s)

    Python Object Serialization - pickle and json

    Python Object Serialization - yaml and json

    Priority queue and heap queue data structure

    Graph data structure

    Dijkstra's shortest path algorithm

    Prim's spanning tree algorithm

    Closure

    Functional programming in Python

    Remote running a local file using ssh

    SQLite 3 - A. Connecting to DB, create/drop table, and insert data into a table

    SQLite 3 - B. Selecting, updating and deleting data

    MongoDB with PyMongo I - Installing MongoDB ...

    Python HTTP Web Services - urllib, httplib2

    Web scraping with Selenium for checking domain availability

    REST API : Http Requests for Humans with Flask

    Blog app with Tornado

    Multithreading ...

    Python Network Programming I - Basic Server / Client : A Basics

    Python Network Programming I - Basic Server / Client : B File Transfer

    Python Network Programming II - Chat Server / Client

    Python Network Programming III - Echo Server using socketserver network framework

    Python Network Programming IV - Asynchronous Request Handling : ThreadingMixIn and ForkingMixIn

    Python Coding Questions I

    Python Coding Questions II

    Python Coding Questions III

    Python Coding Questions IV

    Python Coding Questions V

    Python Coding Questions VI

    Python Coding Questions VII

    Python Coding Questions VIII

    Python Coding Questions IX

    Python Coding Questions X

    Image processing with Python image library Pillow

    Python and C++ with SIP

    PyDev with Eclipse

    Matplotlib

    Redis with Python

    NumPy array basics A

    NumPy Matrix and Linear Algebra

    Pandas with NumPy and Matplotlib

    Celluar Automata

    Batch gradient descent algorithm

    Longest Common Substring Algorithm

    Python Unit Test - TDD using unittest.TestCase class

    Simple tool - Google page ranking by keywords

    Google App Hello World

    Google App webapp2 and WSGI

    Uploading Google App Hello World

    Python 2 vs Python 3

    virtualenv and virtualenvwrapper

    Uploading a big file to AWS S3 using boto module

    Scheduled stopping and starting an AWS instance

    Cloudera CDH5 - Scheduled stopping and starting services

    Removing Cloud Files - Rackspace API with curl and subprocess

    Checking if a process is running/hanging and stop/run a scheduled task on Windows

    Apache Spark 1.3 with PySpark (Spark Python API) Shell

    Apache Spark 1.2 Streaming

    bottle 0.12.7 - Fast and simple WSGI-micro framework for small web-applications ...

    Flask app with Apache WSGI on Ubuntu14/CentOS7 ...

    Fabric - streamlining the use of SSH for application deployment

    Ansible Quick Preview - Setting up web servers with Nginx, configure enviroments, and deploy an App

    Neural Networks with backpropagation for XOR using one hidden layer

    NLP - NLTK (Natural Language Toolkit) ...

    RabbitMQ(Message broker server) and Celery(Task queue) ...

    OpenCV3 and Matplotlib ...

    Simple tool - Concatenating slides using FFmpeg ...

    iPython - Signal Processing with NumPy

    iPython and Jupyter - Install Jupyter, iPython Notebook, drawing with Matplotlib, and publishing it to Github

    iPython and Jupyter Notebook with Embedded D3.js

    Downloading YouTube videos using youtube-dl embedded with Python

    Machine Learning : scikit-learn ...

    Django 1.6/1.8 Web Framework ...











Ph.D. / Golden Gate Ave, San Francisco / Seoul National Univ / Carnegie Mellon / UC Berkeley / DevOps / Deep Learning / Visualization

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Sponsor Open Source development activities and free contents for everyone.

Thank you.

- K Hong







Python tutorial



Python Home

Introduction

Running Python Programs (os, sys, import)

Modules and IDLE (Import, Reload, exec)

Object Types - Numbers, Strings, and None

Strings - Escape Sequence, Raw String, and Slicing

Strings - Methods

Formatting Strings - expressions and method calls

Files and os.path

Traversing directories recursively

Subprocess Module

Regular Expressions with Python

Regular Expressions Cheat Sheet

Object Types - Lists

Object Types - Dictionaries and Tuples

Functions def, *args, **kargs

Functions lambda

Built-in Functions

map, filter, and reduce

Decorators

List Comprehension

Sets (union/intersection) and itertools - Jaccard coefficient and shingling to check plagiarism

Hashing (Hash tables and hashlib)

Dictionary Comprehension with zip

The yield keyword

Generator Functions and Expressions

generator.send() method

Iterators

Classes and Instances (__init__, __call__, etc.)

if__name__ == '__main__'

argparse

Exceptions

@static method vs class method

Private attributes and private methods

bits, bytes, bitstring, and constBitStream

json.dump(s) and json.load(s)

Python Object Serialization - pickle and json

Python Object Serialization - yaml and json

Priority queue and heap queue data structure

Graph data structure

Dijkstra's shortest path algorithm

Prim's spanning tree algorithm

Closure

Functional programming in Python

Remote running a local file using ssh

SQLite 3 - A. Connecting to DB, create/drop table, and insert data into a table

SQLite 3 - B. Selecting, updating and deleting data

MongoDB with PyMongo I - Installing MongoDB ...

Python HTTP Web Services - urllib, httplib2

Web scraping with Selenium for checking domain availability

REST API : Http Requests for Humans with Flask

Blog app with Tornado

Multithreading ...

Python Network Programming I - Basic Server / Client : A Basics

Python Network Programming I - Basic Server / Client : B File Transfer

Python Network Programming II - Chat Server / Client

Python Network Programming III - Echo Server using socketserver network framework

Python Network Programming IV - Asynchronous Request Handling : ThreadingMixIn and ForkingMixIn

Python Coding Questions I

Python Coding Questions II

Python Coding Questions III

Python Coding Questions IV

Python Coding Questions V

Python Coding Questions VI

Python Coding Questions VII

Python Coding Questions VIII

Python Coding Questions IX

Python Coding Questions X

Image processing with Python image library Pillow

Python and C++ with SIP

PyDev with Eclipse

Matplotlib

Redis with Python

NumPy array basics A

NumPy Matrix and Linear Algebra

Pandas with NumPy and Matplotlib

Celluar Automata

Batch gradient descent algorithm

Longest Common Substring Algorithm

Python Unit Test - TDD using unittest.TestCase class

Simple tool - Google page ranking by keywords

Google App Hello World

Google App webapp2 and WSGI

Uploading Google App Hello World

Python 2 vs Python 3

virtualenv and virtualenvwrapper

Uploading a big file to AWS S3 using boto module

Scheduled stopping and starting an AWS instance

Cloudera CDH5 - Scheduled stopping and starting services

Removing Cloud Files - Rackspace API with curl and subprocess

Checking if a process is running/hanging and stop/run a scheduled task on Windows

Apache Spark 1.3 with PySpark (Spark Python API) Shell

Apache Spark 1.2 Streaming

bottle 0.12.7 - Fast and simple WSGI-micro framework for small web-applications ...

Flask app with Apache WSGI on Ubuntu14/CentOS7 ...

Selenium WebDriver

Fabric - streamlining the use of SSH for application deployment

Ansible Quick Preview - Setting up web servers with Nginx, configure enviroments, and deploy an App

Neural Networks with backpropagation for XOR using one hidden layer

NLP - NLTK (Natural Language Toolkit) ...

RabbitMQ(Message broker server) and Celery(Task queue) ...

OpenCV3 and Matplotlib ...

Simple tool - Concatenating slides using FFmpeg ...

iPython - Signal Processing with NumPy

iPython and Jupyter - Install Jupyter, iPython Notebook, drawing with Matplotlib, and publishing it to Github

iPython and Jupyter Notebook with Embedded D3.js

Downloading YouTube videos using youtube-dl embedded with Python

Machine Learning : scikit-learn ...

Django 1.6/1.8 Web Framework ...


Sponsor Open Source development activities and free contents for everyone.

Thank you.

- K Hong






OpenCV 3 image and video processing with Python



OpenCV 3 with Python

Image - OpenCV BGR : Matplotlib RGB

Basic image operations - pixel access

iPython - Signal Processing with NumPy

Signal Processing with NumPy I - FFT and DFT for sine, square waves, unitpulse, and random signal

Signal Processing with NumPy II - Image Fourier Transform : FFT & DFT

Inverse Fourier Transform of an Image with low pass filter: cv2.idft()

Image Histogram

Video Capture and Switching colorspaces - RGB / HSV

Adaptive Thresholding - Otsu's clustering-based image thresholding

Edge Detection - Sobel and Laplacian Kernels

Canny Edge Detection

Hough Transform - Circles

Watershed Algorithm : Marker-based Segmentation I

Watershed Algorithm : Marker-based Segmentation II

Image noise reduction : Non-local Means denoising algorithm

Image object detection : Face detection using Haar Cascade Classifiers

Image segmentation - Foreground extraction Grabcut algorithm based on graph cuts

Image Reconstruction - Inpainting (Interpolation) - Fast Marching Methods

Video : Mean shift object tracking

Machine Learning : Clustering - K-Means clustering I

Machine Learning : Clustering - K-Means clustering II

Machine Learning : Classification - k-nearest neighbors (k-NN) algorithm




Machine Learning with scikit-learn



scikit-learn installation

scikit-learn : Features and feature extraction - iris dataset

scikit-learn : Machine Learning Quick Preview

scikit-learn : Data Preprocessing I - Missing / Categorical data

scikit-learn : Data Preprocessing II - Partitioning a dataset / Feature scaling / Feature Selection / Regularization

scikit-learn : Data Preprocessing III - Dimensionality reduction vis Sequential feature selection / Assessing feature importance via random forests

Data Compression via Dimensionality Reduction I - Principal component analysis (PCA)

scikit-learn : Data Compression via Dimensionality Reduction II - Linear Discriminant Analysis (LDA)

scikit-learn : Data Compression via Dimensionality Reduction III - Nonlinear mappings via kernel principal component (KPCA) analysis

scikit-learn : Logistic Regression, Overfitting & regularization

scikit-learn : Supervised Learning & Unsupervised Learning - e.g. Unsupervised PCA dimensionality reduction with iris dataset

scikit-learn : Unsupervised_Learning - KMeans clustering with iris dataset

scikit-learn : Linearly Separable Data - Linear Model & (Gaussian) radial basis function kernel (RBF kernel)

scikit-learn : Decision Tree Learning I - Entropy, Gini, and Information Gain

scikit-learn : Decision Tree Learning II - Constructing the Decision Tree

scikit-learn : Random Decision Forests Classification

scikit-learn : Support Vector Machines (SVM)

scikit-learn : Support Vector Machines (SVM) II

Flask with Embedded Machine Learning I : Serializing with pickle and DB setup

Flask with Embedded Machine Learning II : Basic Flask App

Flask with Embedded Machine Learning III : Embedding Classifier

Flask with Embedded Machine Learning IV : Deploy

Flask with Embedded Machine Learning V : Updating the classifier

scikit-learn : Sample of a spam comment filter using SVM - classifying a good one or a bad one




Machine learning algorithms and concepts

Batch gradient descent algorithm

Single Layer Neural Network - Perceptron model on the Iris dataset using Heaviside step activation function

Batch gradient descent versus stochastic gradient descent

Single Layer Neural Network - Adaptive Linear Neuron using linear (identity) activation function with batch gradient descent method

Single Layer Neural Network : Adaptive Linear Neuron using linear (identity) activation function with stochastic gradient descent (SGD)

Logistic Regression

VC (Vapnik-Chervonenkis) Dimension and Shatter

Bias-variance tradeoff

Maximum Likelihood Estimation (MLE)

Neural Networks with backpropagation for XOR using one hidden layer

minHash

tf-idf weight

Natural Language Processing (NLP): Sentiment Analysis I (IMDb & bag-of-words)

Natural Language Processing (NLP): Sentiment Analysis II (tokenization, stemming, and stop words)

Natural Language Processing (NLP): Sentiment Analysis III (training & cross validation)

Natural Language Processing (NLP): Sentiment Analysis IV (out-of-core)

Locality-Sensitive Hashing (LSH) using Cosine Distance (Cosine Similarity)




Artificial Neural Networks (ANN)

[Note] Sources are available at Github - Jupyter notebook files

1. Introduction

2. Forward Propagation

3. Gradient Descent

4. Backpropagation of Errors

5. Checking gradient

6. Training via BFGS

7. Overfitting & Regularization

8. Deep Learning I : Image Recognition (Image uploading)

9. Deep Learning II : Image Recognition (Image classification)

10 - Deep Learning III : Deep Learning III : Theano, TensorFlow, and Keras









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