Search results for "python"
epub |eng | | Author:Sanjib Sinha
Understanding Classes and Objects You cannot understand theory unless you implement that concept into the real world. Let us see what we have learned. 1. Classes are when you create ...
( Category:
Hacking
March 21,2017 )
epub, azw3 |eng | 2016-07-18 | Author:Krishna Rungta [Rungta, Krishna]
Summary: Operators in a programming language are used to perform various operations on values and variables. In Python, you can use operators like There are various methods for arithmetic calculation ...
( Category:
other
February 26,2017 )
epub |eng | 2016-07-18 | Author:Unknown
Trick #6: Programming Raspberry PI 3 is also a key factor in helping beginners, even the children, learn more about the field of programming. They can use the Raspberry PI ...
( Category:
Single Board Computers
February 26,2017 )
epub, pdf |eng | 2016-07-06 | Author:Jonathan Yates [Yates, Jonathan]
Basic lists operations Like Python strings, we can use + and * operators on lists for operations like concatenation and repetition respectively. Below are the list operations on Python lists. ...
( Category:
Introductory & Beginning
February 26,2017 )
mobi |eng | | Author:unknow
Access to variables defined outside the local namespace Python allows functions to access variables defined in any of its enclosing program units. These are called global variables, in contrast to ...
( Category:
other
February 25,2017 )
mobi, azw3, epub |eng | 2016-08-07 | Author:John V. Guttag
A rooted binary tree is an acyclic directed graph in which There is exactly one node with no parents. This is called the root. Each non-root node has exactly one ...
( Category:
Python
February 10,2017 )
epub |eng | | Author:Magnus Vilhelm Persson
As for the final analysis, two different clusters are identified. Just as our previous hypothesis, there is a cluster with a clear linear trend with relatively higher GDP, which is ...
( Category:
Data Processing
January 21,2017 )
epub, pdf, azw3 |eng | | Author:Dipanjan Sarkar
def tfidf_transformer(bow_matrix): transformer = TfidfTransformer(norm='l2', smooth_idf=True, use_idf=True) tfidf_matrix = transformer.fit_transform(bow_matrix) return transformer, tfidf_matrix You can see that we have used the L2 norm option in the parameters and also made ...
( Category:
Python
January 9,2017 )
epub |eng | 2016-11-20 | Author:VanderPlas, Jake [VanderPlas, Jake]
In [ 4 ]: for i in range ( 1 , 7 ): plt . subplot ( 2 , 3 , i ) plt . text ( 0.5 , 0.5 ...
( Category:
Research
November 25,2016 )
epub |eng | | Author:Marco Bonzanini
Visualizing posts as a word cloud After analyzing interactions, we move our attention back to the content of the posts. Word clouds, also called tag clouds (https://en.wikipedia.org/wiki/Tag_cloud), are visual representations ...
( Category:
Python
September 21,2016 )
epub |eng | | Author:unknow
Traditional models might try to associate particular features with each target (much like our feature matching approach in Chapter 3, Finding Objects via Feature Matching and Perspective Transforms), which would ...
( Category:
other
August 19,2016 )
epub, azw3, mobi, pdf |eng | 2016-03-11 | Author:LazyProgrammer [Unknown]
I create a train function similar to the simple example above: train = theano.function( inputs=[thX, thT], updates=[(W1, update_W1),(W2, update_W2)], ) And I create a prediction function to tell me the ...
( Category:
other
July 1,2016 )
epub, pdf |eng | 2016-05-15 | Author:LazyProgrammer [Unknown]
So in Theano, our first filter has the dimensions “num_feature_maps”, which you can think of as the number of kernels or filters we are going to create, then it has ...
( Category:
other
July 1,2016 )
epub |eng | 2016-05-17 | Author:LazyProgrammer [Unknown]
If you haven’t installed NLTK already, you can just do “sudo pip install nltk” in your command line. Note that for some of these examples, NLTK might give you a ...
( Category:
other
July 1,2016 )
epub |eng | 2016-05-22 | Author:LazyProgrammer [Unknown]
N = 900 # number of samples X = np.zeros((N, D)) X[:300, :] = np.random.randn(300, D) + mu1 X[300:600, :] = np.random.randn(300, D) + mu2 X[600:, :] = np.random.randn(300, D) ...
( Category:
other
July 1,2016 )
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