[API Days] Cooking with apis

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This document discusses natural language processing and deep learning techniques for analyzing text and images. It describes how APIs can extract keywords, topics, themes and categories from documents and images. Examples are given of extracting descriptions from images using these techniques, as well as analyzing user interests based on Instagram photos. The document promotes two people from the company Beeva who work on natural language processing and deep learning projects.

Technology

Avenida
de
Burgos
16
D,
28036
Madrid

hablemos@beeva.com

www.beeva.com

BEE
PART
OF
THE
CHANGE

COOKING
WITH
APIS

CREATING
VALUE
WITH
IMAGES
AND
WORDS

NIEVES
ÁBALOS

ENRIQUE
OTERO

Natural
Language
Processing

InformaNon
ExtracNon
problem

Link
to
slides
about
word
vectors
from
NIPS
2013
Deep
Learning
Workshop:
NNforText.pdf

Word
Vector
Space

Natural
Language
Processing

word2vec
(Google)

RegulariNes
in
Word
Vector

Space

Natural
Language
Processing

word2vec
(Google)

grammaNcal
gender:

masculine
&
feminine

Link
to
slides
about
word
vectors
from
NIPS
2013
Deep
Learning
Workshop:
NNforText.pdf

RegulariNes
in
Word
Vector

Space

Natural
Language
Processing

word2vec
(Google)

...
Vector
space
operaNons

deducNon
@
context:

country
&
capital

Link
to
slides
about
word
vectors
from
NIPS
2013
Deep
Learning
Workshop:
NNforText.pdf

NLP

Our
API
extracts

from
URLs/docs

Language

Keywords
EnNNes

Concepts
Related
words

Topic
Theme

Theme
extracNon
Category
AcNon

COMINGSOON!

PROOF
OF
CONCEPT

Summarize
and
categorize
documents

keywords

topic

(category)

theme

(1-‐word
summary)

(*)
there
are
two
men
who
appear

to
be
pracZcing
marZal
arts

(*)
a
cat
si[ng
inside
of

a
piece
of
luggage

(*)
real
examples
extracted
from
hp://deeplearning.cs.toronto.edu/i2t

...
unNl
NOW!

PROOF
OF
CONCEPT

Analyzing
user
interests
based
on

Instagram
pictures

ANIMALS
NATURE
TOURISM
SHOWS/EVENTS
FOOD
SPORTS
BEAUTY
12
custom
categories

1000
predeﬁned
labels

(wordnets)

3rd-party Deep Learning API
stage
restaurant

Fast
validaNon
and
back
to
the
kitchen...

www.beeva.com

hablemos@beeva.com

Enrique
Otero

Cooking
with
images

enrique.otero@beeva.com

Nieves
Ábalos

Cooking
with
words

nieves.abalos@beeva.com

@nieves_as

Thank
you!

QuesNons?

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[API Days] Cooking with apis

1. Avenida de Burgos 16 D, 28036 Madrid hablemos@beeva.com www.beeva.com BEE PART OF THE CHANGE COOKING WITH APIS CREATING VALUE WITH IMAGES AND WORDS NIEVES ÁBALOS ENRIQUE OTERO

2. I’m Xavi

7. Let’s cook!

10. Let’s try another recipe

11.

12.

13.

14.

15.

16. a few days ago...

17.

18.

19.

20.

21. NOW! HOW? sustainable WOW!

22.

23.

24. Natural Language Processing InformaNon ExtracNon problem

25. Link to slides about word vectors from NIPS 2013 Deep Learning Workshop: NNforText.pdf Word Vector Space Natural Language Processing word2vec (Google)

26. RegulariNes in Word Vector Space Natural Language Processing word2vec (Google) grammaNcal gender: masculine & feminine Link to slides about word vectors from NIPS 2013 Deep Learning Workshop: NNforText.pdf

27. RegulariNes in Word Vector Space Natural Language Processing word2vec (Google) ... Vector space operaNons deducNon @ context: country & capital Link to slides about word vectors from NIPS 2013 Deep Learning Workshop: NNforText.pdf

28. NLP 3rd party APIs

29. NLP Our API extracts from URLs/docs Language Keywords EnNNes Concepts Related words Topic Theme Theme extracNon Category AcNon COMINGSOON!

30. PROOF OF CONCEPT Summarize and categorize documents keywords topic (category) theme (1-‐word summary)

31. An image is worth 1000 words

32. But... not for machines

33. (*) there are two men who appear to be pracZcing marZal arts (*) a cat si[ng inside of a piece of luggage (*) real examples extracted from hp://deeplearning.cs.toronto.edu/i2t ... unNl NOW!

34. DEEP LEARNING APIs COMINGSOON! and...

35. PROOF OF CONCEPT Analyzing user interests based on Instagram pictures

36. ANIMALS NATURE TOURISM SHOWS/EVENTS FOOD SPORTS BEAUTY 12 custom categories 1000 predeﬁned labels (wordnets) 3rd-party Deep Learning API stage restaurant

37.

38.

39.

40. Fast validaNon and back to the kitchen...

41. ... and to the garden

42.

43. www.beeva.com hablemos@beeva.com Enrique Otero Cooking with images enrique.otero@beeva.com Nieves Ábalos Cooking with words nieves.abalos@beeva.com @nieves_as Thank you! QuesNons?

[API Days] Cooking with apis

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