From power chords to the power of models

From
Power Chords
to
the Power of
Models
@aliostad
Ali Kheyrollahi

8
Local pop music
?
9
Local pop music
“Cheelee pom!”
10
Boney M
“Rasputin”
11
Blondie
“Heart of Glass”

Data Source - Wiki
4,990,2794,990,279 English Articles
37,583,879 Articles

Data Source - What about …
2012
http://money.cnn.com/2012/03/13/technology/encyclopedia-britannica-books/

Data Source - Wiki vs Britannica
Feng Zhu (assistant prof at Harvard):
There has been lots of research on the accuracy of
Wikipedia, and the results are mixed—some studies
show it is just as good as the experts, others show
[that] Wikipedia is not accurate at all.
… the editors [of Britannica] are still not
found to be more objective than the crowd
in articles that are suﬃciently revised.

Data Source - Wikipedia in scholar papers
0
45000
90000
135000
180000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Source: Google Scholar

Data Source - Dark side of Wiki
The Doggs: A mid-1960s British rock band, noted for
barking while playing music
Lord Byron kept a crocodile and a honey badger as a pet
Ted and the Treble Tones, a rock band from Fresno with a
1959 hit single that appeared nowhere on the Billboard
charts
The Shaggs all female (sister) band Hacked-at drumbeats,
whacked-around chords, songs that seem to have little or
no meter to them… Zappa’s favourite…

Data Source - Content vs. Data
… or … Alt-country
Alternative country
… or … Alt.country
… or … Alt. Country
[mind the gap]

comma
slash
new line
period?
no, greek middle dot U+00B7

Hyphen
U+002D
ﬁgure dash
U+2012
minus sign
U+2015
em dash
U+2014
en dash
U+2013

Data Source - DBpedia
• http://wiki.dbpedia.org/
• bziped N-Triples or N-Quads RDF entries
• Different datasets:
• Titles
• Categories
• Infobox data
• Short Abstracts
• In the end was not used

Data Acquisition - Wiki
List of Rock
Genres
Rock Genres Rock Artists
Store
Store
HTML
Capture
Links
Store
HTML
Python scripts
Postgres

Data Exploration
“I personally … literally just look at the screen,
just like the matrix”
Claudia Perlich, multi-award winner Data
Scientist

Data Exploration
“… the dirty little secret that I have won all of them
because I have found something wrong with the
data… I would like to play around with dataset and
get initimately familiar with dataset and its
properties.“
Claudia Perlich

50 Years of Rock 1965
1. rock (23)
2. folk rock (15)
3. rock and roll (13)
4. folk (12)
5. rhythm and blues (12)
6. blues rock (10)
7. country (9)
The Who
My Generation
“Rock” as genre was king with The
likes of The Who and Beatles
Rock and Roll, popular then, soon to
disappear from the list

1. folk rock (25)
2. garage rock (21)
3. psychedelic rock (20)
4. rock (19)
5. folk (14)
6. protopunk (11)
7. pop (11)
Bob Dylan
Blonde on Blonde
Folk Rock was popular all of the
sixties with a resurgence in the early
70s
This is obviously named
retrospectively but along with
Garage rock to influence punk

1. psychedelic rock (56) ↑
2. folk rock (30)
3. pop (19)
4. folk (19)
5. rock (18)
6. garage rock (12)
7. psychedelic pop (12) The Beatles
Sgt. Pepper's Lonely Hearts
Club Band
67, a big year with Monterey Pop
Festival, was all about psychedelia
Even for psychedelic pop

1. psychedelic rock (70) ←
2. rock (29)
3. folk rock (26)
4. blues rock (25)
5. folk (18)
6. acid rock (18)
7. progressive rock (15)
Jimi Hendrix
Electric Ladyland
No comment, listen to Jimi!
Note Acid Rock and emergence of
progressive rock

1. psychedelic rock (60) ←
2. rock (40)
3. folk rock (33)
4. hard rock (27)
5. folk (26)
7. country (22)
Led Zeppelin
Led ZeppelinNot much changed… progrock
moving up

1. rock (57)
2. folk rock (47) ↑
3. psychedelic rock (44) !!
4. hard rock (40) ↑
5. progressive rock (31) ↑
6. country rock (25)
7. folk (24) The Velvet Underground
Loaded
Artists leaving psychedelia for a
harder sound

1. rock (47)
3. folk rock (33) !
4. progressive rock (33) ↑
5. psychedelic rock (26) !
6. folk (24)
7. country rock (18) CAN
Tago Mago
Progressive rock and hard rock
moving up

1. rock (54)
2. folk rock (34)
4. hard rock (29)
6. folk (25)
7. country (20)
Jethro Tull
Thick as a Brick

1. rock (60)
4. folk rock (29)
5. country (24)
6. glam rock (22)
Flashy dresses and make-up, bring it
on!
Pink Floyd
Dark Side of the Moon

1. rock (58)
2. hard rock (52)
4. glam rock (29) ↑
5. folk rock (28)
6. blues rock (20)
Supertramp
Crime of the Century

1. rock (53)
2. hard rock (52)
4. folk rock (23)
5. country (20)
6. blues rock (20)
Patti Smith
Horses

1. rock (71)
2. hard rock (56)
4. heavy metal (19)
5. pop (19)
6. folk (18)
7. country (18)
Did you notice?! Heavy Metal is now
a thing…
AC/DC
High Voltage

1. rock (77)
2. hard rock (43)
4. punk rock (21)
5. pop (19)
6. folk rock (15)
Oh yeah, punk rock in full swing and
pretty much mainstream.
Sex Pistols
Never Mind the Bollocks

1. rock (77)
2. hard rock (40)
4. new wave (23)
5. pop (21)
6. country (20)
7. punk rock (19)
New wave with a more layered
sound replacing punk rock
Talking Heads
More Songs About Buildings and
Food

1. rock (68)
2. new wave (49) ↑
3. post-punk (42)
4. hard rock (38) !
5. country (22)
6. punk rock (20)
7. progressive rock (15) !
Only after two years of punk, we
have post-punk… phew
Gary Numan
Replicas

1. rock (66)
2. new wave (57)
3. post-punk (49)
4. hard rock (38)
5. country (24)
6. heavy metal (22)
7. punk rock (21)
Joy Division
Closer

1. new wave (75) ↑
2. rock (71)
3. post-punk (68)
4. hard rock (41)
5. synthpop (25)
6. heavy metal (21)
7. country (19)
Another movement is already in
motion with softer keyboard-based
sound
Klaus Nomi
Klaus Nomi

1. new wave (79)
2. post-punk (61)
3. rock (61)
4. hard rock (37)
5. heavy metal (28) ↑
6. synthpop (28)
7. hardcore punk (21)
Iron Maiden
Number of the Beast
Other than new wave and post-punk,
two completely different sounds
growing, heavy metal on one hand
and synth pop on the other…

1. rock (74)
2. new wave (71)
3. post-punk (45) !
4. synthpop (39) ↑
5. heavy metal (36)
6. hard rock (35)
7. pop rock (24) The Police
Synchronicity

1. new wave (73)
2. rock (61)
3. post-punk (44)
4. heavy metal (37)
5. synthpop (37)
6. hard rock (26)
7. alternative rock (24)
In case you haven’t noticed, New
Wave has been on the top spot for a
few years
Echo and the Bunnymen
Ocean Rain

1. new wave (66)
2. rock (56)
3. post-punk (45)
4. heavy metal (33)
5. pop rock (33)
6. alternative rock (28) ↑
7. synthpop (28) !
Alternative Rock appearing… soon
to label much of the rock music we
have now…
The Fall
This Nation’s Saving Grace

1. rock (68)
2. new wave (48) !
4. heavy metal (42)
5. post-punk (40)
6. hard rock (31)
7. synthpop (28)
Synthpop is over… only to come
back after 25 years.
The Smiths
The Queen Is Dead

1. rock (62)
3. hard rock (36)
4. thrash metal (35)
5. heavy metal (31)
6. new wave (29) !
7. pop rock (24)
Wow… where did this come from?!
Testament
The Legacy

2. rock (58)
3. heavy metal (42)
4. hard rock (40)
6. synthpop (24)
7. new wave (23) !
New wave is out of favour after
almost 10 years…
Sonic Youth
Daydream Nation

1. rock (80)
3. hard rock (41)
5. heavy metal (34)
6. new wave (26)
7. pop (22)
The Cure
Disintegration

Data Models Model
Mathematical representation of a concept
based on parameters that impact that
concept
• Rating of a native app
• Stackoverﬂow score
• Credit score
• Fraud check

Data Models Model Example
(Average)

Data Models Graph 101
Social Network Analysis
and Graph Theory
• Nodes/vertices and edges/lines
• Directedness:
• Directed
• Undirected
• Degree, InDegree/OutDegree
• Weight
A B

Data Models Centrality
API - Related artists
1
2
4
2
2
1
Same degree
Different betweenness
Degree

Graph Codez
import networkx as nx
g = nx.Graph()
g.add_edge(‘a’, ‘b’)
g.add_edge(‘b’, ‘c’)
…
print len(g[‘b’]) # degree
c = nx.betweenness_centrality(g, normalized=True)
# c -> dictionary of node names and their score
DiGraph()

Modelling
Inﬂuence
using
Wiki

Data Models Cited Inﬂuence
Howlin’
Wolf
Captain
Beefheart
1940 1964

Most inﬂuential Rock Artists Based on out-degree
The Beatles => 188
Black Sabbath => 127
Led Zeppelin => 118
Jimi Hendrix => 114
Bob Dylan => 94
Pink Floyd => 86
Iron Maiden => 77
Metallica => 77
The Rolling Stones => 66
The Beach Boys => 65
Neil Young => 63
Nirvana => 62
Slayer => 60
Queen => 59

Most inﬂuential Rock Artists Based on Betweenness Centrality
Jimi Hendrix => 53476.2014921
The Beatles => 47511.7957531
Bob Dylan => 38107.0298185
Led Zeppelin => 32701.7223273
Nirvana => 29733.9066836
Metallica => 29356.6009213
Queen => 28989.2844223
Robert Smith => 28880.670718
Elvis Presley => 28463.2891497
Slade => 27656.487307
Iron Maiden => 22449.6697023
Ramones => 22437.6112965
Rush => 21125.9481602
Neil Young => 19913.887522

Most influential Artists Based on Betweenness Centrality
Metallica => 566.06
Iron Maiden => 419.21
Corey Taylor => 146.0
Led Zeppelin => 122.73
Slipknot => 116.58
King Diamond => 94.7
Machine Head => 85.12
Rush => 70.41
Black Sabbath => 68.0
Van Halen => 54.56
Deep Purple => 53.5
Megadeth => 42.63
Guns N' Roses => 24.25
Heavy Metal
Nirvana => 490.08
Muse => 114.5
Weezer => 97.33
Pixies => 94.17
Sonic Youth => 78.5
Rivers Cuomo => 69.5
Siouxsie and the Banshees => 51.67
The Smiths => 51.5
Jeff Buckley => 46.17
The Offspring => 43.0
Placebo => 42.0
My Chemical Romance => 34.0
The Smashing Pumpkins => 32.33
Alternative Rock
Rush => 54.0
Marillion => 34.0
Pink Floyd => 33.0
Yes => 20.0
Porcupine Tree => 19.5
Dream Theater => 19.0
Chris Squire => 16.5
Primus => 15.0
Tool => 12.0
Mahavishnu Orchestra => 8.0
Geddy Lee => 7.0
Neil Peart => 5.0
Keith Emerson => 5.0
Progressive Rock

Shaping a Model
•Start with Literature survey
•Draw parallels with similar domains
•If you still cannot find one, define a
new model
•Try to refine your model with a
quantitative measure if available

Shaping Visionaries Model
Visionary: Inﬂuencing vs. being inﬂuenced
V: visionary factor
deg+: out-degree
deg-: in-degree
e: the exponential function

Shaping Visionaries Model
Joni Mitchell => 44.0
Roxy Music => 31.0
Leonard Cohen => 31.0
Josh White => 31.0
Robert Johnson => 24.0
Etta James => 23.0
Discharge => 22.0
Sam Cooke => 21.0
Duke Ellington => 20.0
Marc Bolan => 19.0
Hank Marvin => 17.0
Jonathan Richman => 16.0
Scott Walker => 16.0
Testament => 16.0
The Police => 16.0
Skip James => 15.0
Albert King => 15.0
The Cure => 14.7151776469
Nico => 14.0

Data Models Visionaries Model
Most inﬂuential Artists in genres
Radiohead => 15.0
Depeche Mode => 12.0
Morrissey => 11.0
Robert Smith => 11.0
The Replacements => 10.0
The Cure => 8.83
XTC => 8.0
Stone Temple Pilots => 6.0
Duran Duran => 6.0
Nick Cave => 6.0
Wipers => 5.0
Johnny Marr => 4.0
Jane's Addiction => 4.0
Alternative Rock
The Stooges => 6.0
The Jam => 6.0
Sex Pistols => 4.05
Jawbreaker => 4.0
The Runaways => 4.0
The Dickies => 4.0
Circle Jerks => 3.0
Big Black => 3.0
Patti Smith => 3.0
Elvis Costello => 3.0
The Only Ones => 3.0
Kevin Seconds => 3.0
Descendents => 2.94
Punk Rock
Captain Beefheart => 7.0
The Magic Band => 6.0
Genesis => 5.0
Scott Walker => 5.0
Scott Engel => 3.0
Talk Talk => 3.0
Sparks => 2.0
Richard Wright => 2.0
Roxy Music => 1.84
Pink Floyd => 1.49
Mark Hollis => 1.0
Nico => 1.0
Lou Reed => 1.0
Art Rock
Dave Lombardo => 11.0
Napalm Death => 8.0
Morbid Angel => 3.68
Possessed => 3.31
Carcass => 2.21
Immolation => 2.0
Vulcano => 2.0
Hellhammer => 2.0
Autopsy => 1.47
Repulsion => 1.1
Entombed => 1.1
Impetigo => 1.0
Nihilist => 1.0
Death Metal

Shaping Impact Model
Imact: Inﬂuencing many in a short span
Imp: impact factor
deg+: out-degree
L: longevity (initial continuous years active)

Imact: Inﬂuencing many in a short span
Cream => 3.33
Sex Pistols => 2.19
Joy Division => 2.0
Jimi Hendrix => 1.83
The Beatles => 1.59
The Smiths => 1.19
Big Star => 1.19
Parliament => 1.11
Nirvana => 1.0
Ritchie Valens => 0.78
Traﬃc => 0.67

Imact: Influencing many in a short span in a specific genre
Nirvana => 0.5
The Smiths => 0.5
Pixies => 0.28
Happy Mondays => 0.11
Jeff Buckley => 0.1
Jane's Addiction => 0.08
Soundgarden => 0.08
Violent Femmes => 0.06
A Perfect Circle => 0.06
Sublime => 0.05
The Hush Sound => 0.04
The Get Up Kids => 0.03
Alternative Rock
Sex Pistols => 0.69
Fuel => 0.22
Descendents => 0.16
The Runaways => 0.16
The Clash => 0.16
Hot Water Music => 0.13
The Stooges => 0.09
Buzzcocks => 0.08
Misfits => 0.06
Ramones => 0.05
The Jam => 0.05
Dead Kennedys => 0.05
Punk Rock
Cream => 1.67
Jimi Hendrix => 0.56
Thin Lizzy => 0.39
Deep Purple => 0.27
Ram Jam => 0.25
The Stooges => 0.11
Jim Morrison => 0.09
The Runaways => 0.08
Alice Cooper => 0.08
The Doors => 0.06
The Who => 0.04
Judas Priest => 0.02
Hard Rock
Joy Division => 0.56
Bauhaus => 0.028
The Police => 0.02
Siouxsie and Banshees => 0.02
Robert Smith => 0.006
Ultravox => 0.005
New Order => 0.005
My Bloody Valentine => 0.004
Sonic Youth => 0.004
Talking Heads => 0.003
Nick Cave => 0.002
The Cure => 0.001
David Byrne => 0.001
Post-punk

Weighted graph Album Genres
Krautrock
Psychedelic Rock
Experimental
Rock
1
1
1

Genre Aﬃnity
Alternative Rock
Art Rock
Experimental Rock
Progressive Rock
29
47
31
27
Indie Rock

Genre Aﬃnity
Indie Rock
Shoegazing
Alternative Rock
Dream Pop
22
25
24
12
Post-rock

Genre Aﬃnity
Gothic Metal
Doom Metal
Black Metal
Heavy Metal
13
34
27
12
Stoner Metal

Clustering in Networks
u1 u2 u3 u4 u5
u1 2
u2 3
u3 2
u4 2
u5 3
Spectral Clustering:
Using Eigenvectors of the Laplacian Matrix
−
u1 u2 u3 u4 u5
u1 1 0 0 1
u2 1 1 1 0
u3 0 1 0 1
u4 0 1 0 1
u5 1 0 1 1
=
u1 u2 u3 u4 u5
u1 2 -1 0 0 -1
u2 -1 3 -1 -1 0
u3 0 -1 2 0 -1
u4 0 -1 0 2 -1
u5 -1 0 -1 -1 3
Degree Matrix
Adjacency Matrix
(Similarity Matrix)
Laplacian Matrix

Clustering in Networks
Eigenvector: a vector (v) that by getting multiplied in matrix A
does not result in changing its direction (similar to being
multiplied by scalar λ)
u1 u2 u3 u4 u5
-0.7 0.3 -0.2 -0.1 0.7
-0.7 0.3 -0.2 -0.1 0.7

Spectral Clustering Codez
from sklearn.cluster import spectral_clustering
import numpy as np
A = [[0.0 for x in n] for x in n]
… # build adjacency matrix
res = spectral_clustering(np.matrix(A),
n_clusters)
# res -> list of cluster indices e.g. [1,1,0,5,…]

Spectral Clustering Results
Folk Rock
Country Rock
Blues
Folk
Country
Americana
Roots Rock
Blues Rock
Southern Rock
Power Metal
Progressive Metal
Symphonic Metal
Black Metal
Melodic Death Metal
Groove Metal
Nu Metal
Thrash Metal
Death Metal
Metalcore
Industrial Metal
Gothic Metal
Christian Metal
Doom Metal
Speed Metal
Alternative Rock
Indie Rock
New Wave
Synthpop
Electronica
Rock
R&B
Pop
Pop Rock
Funk
Soul
Heavy Metal
Hard Rock
Alternative Metal

2. rock (59)
3. hard rock (48)
5. heavy metal (36)
6. punk rock (25)
7. country (23)
This will stay at this spot pretty much
most of the remaining 25 years…
The Black Crowes
Shake Your Money Maker

2. rock (68)
3. heavy metal (44)
4. hard rock (33)
6. country (23)
7. pop (19)
Nirvana
Nevermind

2. rock (56)
3. heavy metal (37)
4. hard rock (35)
6. death metal (27)
7. indie rock (25)
A new genre is born….
Rage Against The Machine
Rage Against The Machine

2. rock (72)
3. indie rock (39)
4. pop (28)
5. hard rock (28)
6. punk rock (28)
7. grunge (26)
It is all about the West Coast now,
especially Seattle…
Tool
Undertow

2. rock (81)
3. indie rock (52)
4. punk rock (46) ↑
5. heavy metal (45)
6. hard rock (34)
7. country (24)
It is popular again and will be for the
next 10 years…
Soundgarden
Superknown

2. rock (68)
3. punk rock (49)
4. heavy metal (46)
5. indie rock (44)
6. hard rock (37)
7. grunge (28)
The Smashing Pumpkins
Mellon Collie and the Inﬁnite
SadnessAs it is clear here, grunge was really
undercurrent of what was going at
the time although first half of the 90s
known to be the grunge half-decade

2. rock (98)
3. indie rock (60) ↑
4. punk rock (44)
5. heavy metal (42)
6. hard rock (28)
7. country (27)
Another generic label not fully
describing a genere…
Nick Cave and the Bad Seeds
Murder Ballads

2. rock (78)
3. indie rock (60)
4. punk rock (45)
5. heavy metal (33)
6. alternative metal (23)
7. pop punk (22)
Radiohead
OK Computer

2. rock (73)
3. indie rock (69)
4. punk rock (41)
5. heavy metal (37)
6. hard rock (29)
7. black metal (28)
Harder sound is more popular and
we have a new genre in the list
Neutral Milk Hotel
In the Aeroplane Over the Sea

2. indie rock (73)
3. rock (72)
4. heavy metal (40)
5. hard rock (40)
7. punk rock (33)
The Beta Band
The Beta Band
We really did not see Britpop making
an appearance… although the
impact and influence would exist

2. indie rock (88)
3. rock (86)
4. punk rock (45)
5. heavy metal (41)
6. pop punk (37)
Grandaddy
The Sophtware Slump

2. rock (94)
3. indie rock (83)
5. nu metal (45)
6. hard rock (44)
7. punk rock (36)
Another metal subgenre appearing
and will not be the last.
System of a Down
Toxicity

2. indie rock (107)
3. rock (73)
4. heavy metal (54)
6. hard rock (48)
7. punk rock (45)
Interpol
Turn on the Bright Lights

2. indie rock (117)
3. rock (91)
5. punk rock (53)
6. hard rock (51)
7. power metal (46)
The White Stripes
Elephant

2. indie rock (130)
3. rock (96)
4. hard rock (64)
6. punk rock (51)
7. pop punk (49)
Not much changing in the list…
Arcade Fire
Funeral

2. indie rock (164)
3. rock (107)
4. hard rock (69)
5. heavy metal (57)
6. pop punk (55)
Sufjan Stevens
Illinoise

2. indie rock (179)
3. rock (105)
4. hard rock (70)
5. pop punk (58)
6. progressive metal (55)
7. power metal (46)
And yet another metal subgenre
hitting mainstream…
MUSE
Black Holes and Revelations

2. indie rock (201)
3. rock (109)
4. hard rock (67)
6. heavy metal (62)
7. pop punk (59)
Radiohead
In Rainbows

2. indie rock (197)
3. rock (101)
4. hard rock (60)
5. pop (56)
6. metalcore (51)
7. heavy metal (50)
And we have another metal variant
that is here to stay…
Underoath
Lost in the Sound of Separation

2. indie rock (194)
3. rock (72)
4. hard rock (68)
5. indie pop (59)
6. pop rock (59)
7. metalcore (56)
Softer sounds come back…
Animal Collective
Merriweather Post Pavilion

2. indie rock (163)
3. rock (86)
4. hard rock (77)
5. indie pop (64)
6. heavy metal (62)
7. metalcore (61)
… and will be around… Arcade Fire
The Suburbs

2. indie rock (162)
3. metalcore (63)
4. indie pop (60)
6. heavy metal (57)
7. rock (57)
Tinariwen
Tassili

2. indie rock (127)
3. rock (71)
4. hard rock (69)
6. metalcore (49)
7. indie pop (45)
Goat
World Music

2. indie rock (124)
3. rock (73)
4. hard rock (57)
5. synthpop (56)
6. metalcore (51)
7. indie pop (51)
And synthpop makes a come back
after 30 years or so…
Queens of the Stone Age
...Like Clockwork

2. indie rock (106)
3. rock (50)
4. hard rock (48)
5. pop (41)
6. metalcore (41)
7. pop rock (40)
Mac DeMarco
Salad Days

What is an Intelligent Model?
•Intelligent model is actually not a technically accurate term (but 
we use it to denote shift in technique)
•Models learn from data instead of arbitrary deﬁnition
•Can sometimes even distinguish and ﬁlter noise
•Can represent a far more complex data relationships
•Examples include HMM, SVM and recently Deep Learning
•We review word2wec which uses Neural Network  
technique but not as deep as “Deep Learning”

word2vec Model
A very powerful model representing words (tokens) as vectors
based on their proximity to other words (tokens).
Once represented as vectors, all algebraic operations can be applied
to the vector representations.

word2vec Model
Skip-gram: a proximity-based probability model trained
using Neural Networks (Deep Learning)
Pink Floyd were an English rock band formed in London
X XX

word2vec Model
Break the
text to
sentences
Tokenise
each
sentence to
word/
phrases
Feed the
data to
engine
Store
the
Model
Training
Load the
model
Construct
the query
Run against
the model
(Filter noise)
Querying

word2vec Codz (Training)
import gensim
def train_and_save(multiword_dic_path, stopword_path,
corpus_files_glob_pattern, mode_file_name = ‘rock_music.w2v'):
tknsr =
dictionary_tokenization.DictionaryBasedMultiwordFinder(dictionary_path
=multiword_dic_path)
filtr = stopword_filtering.StopwordFilter(stopword_path)
tokenizze = lambda text: filtr.filter(tknsr.tokenise(text))
wvc =
gensim.models.Word2Vec(EnglishSentences(corpus_files_glob_pattern,
tokenizze, True), min_count=2)
wvc.save(mode_file_name)

word2vec Codz (Querying)
sims = word_sim.most_similar(positive=pos, negative=neg, topn=topn)
# pos is an array of positive e.g. [“Nick Mason”, “The Rolling Stones”]
# neg is an array of negative e.g. [“Pink Floyd”]
# result is an array of tuples of results for
# “Nick Mason” + “The Rolling Stones” - “Pink Floyd”
# the first entry of which is “Charlie Watts”

References
•All pictures from wikipedia.org used under Creative Commons
•Source of all data is from wikipedia.org collected online using a single call and then stored and processed
•Eﬃcient Estimation of Word Representations in Vector Space. Mikolov et. al. http://arxiv.org/abs/1301.3781
•Gensim's word2vec
•networkx lib
•word2vec blog post (500K docs): Five crazy abstractions my Deep Learning word2vec model just did
•word2vec on Rock music blog: Daft Punk+Tool=Muse: word2vec model trained on a small Rock music corpus
•code for word2vec on wiki data
•Highcharts: highcharts
•word2vec paper: PDF
•Automatic real-time road marking recognition using a feature-driven approach PDF
•Video of the road marking recognition: here and here and here
•Future of Programming - Rise of the Scientiﬁc Programmer (and fall of the craftsman)

From power chords to the power of models

Recommended

Recommended

More Related Content

Similar to From power chords to the power of models

Similar to From power chords to the power of models (20)

More from Ali Kheyrollahi

More from Ali Kheyrollahi (18)

Recently uploaded

Recently uploaded (20)

From power chords to the power of models