Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Deep Learning and Natural Language Processing w...
Search
Melanie Warrick
June 17, 2016
Technology
1
450
Deep Learning and Natural Language Processing with Spark - Berlin
Melanie Warrick
June 17, 2016
Tweet
Share
More Decks by Melanie Warrick
See All by Melanie Warrick
PyCon.ru RL Talk Resources
nyghtowl
0
93
Career Path Advice
nyghtowl
0
200
AI & Enterprise
nyghtowl
0
310
Artificial Intelligence
nyghtowl
2
770
Reinforcement Learning
nyghtowl
2
470
Machine Learning
nyghtowl
0
330
Machine Learning Resources
nyghtowl
0
89
What is AI? - Jerusalem
nyghtowl
0
260
Computer Vision Deep Learning with DL4J
nyghtowl
0
540
Other Decks in Technology
See All in Technology
ルートユーザーの活用と管理を徹底的に深掘る
yuobayashi
6
680
AWS CDK コントリビュート はじめの一歩
yendoooo
1
100
技術的負債を正しく理解し、正しく付き合う #phperkaigi / PHPerKaigi 2025
shogogg
7
1.6k
ペアプログラミングにQAが加わった!職能を超えたモブプログラミングの事例と学び
tonionagauzzi
1
110
Compose MultiplatformにおけるiOSネイティブ実装のベストプラクティス
enomotok
1
180
空が堕ち、大地が割れ、海が涸れた日~もしも愛用しているフレームワークが開発停止したら?~ #phperkaigi 2025
77web
2
960
fukuoka.ts #3 社内でESLintの共通設定を配りたい2025年春版
pirosikick
1
280
KCD Brazil '25: Enabling Developers with Dapr & Backstage
salaboy
1
120
技術好きなエンジニアが _リーダーへの進化_ によって得たものと失ったもの / The Gains and Losses of a Tech-Enthusiast Engineer’s “Evolution into Leadership”
kaminashi
0
180
製造業の会計システムをDDDで開発した話
caddi_eng
3
800
3/26 クラウド食堂LT #2 GenU案件を通して学んだ教訓 登壇資料
ymae
1
130
EMの仕事、あるいは顧客価値創出のアーキテクト
radiocat
0
130
Featured
See All Featured
How to Think Like a Performance Engineer
csswizardry
22
1.4k
Imperfection Machines: The Place of Print at Facebook
scottboms
267
13k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
46
2.4k
GitHub's CSS Performance
jonrohan
1030
460k
Music & Morning Musume
bryan
46
6.4k
Making Projects Easy
brettharned
116
6.1k
Navigating Team Friction
lara
183
15k
RailsConf 2023
tenderlove
29
1k
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.2k
Product Roadmaps are Hard
iamctodd
PRO
52
11k
Refactoring Trust on Your Teams (GOTO; Chicago 2020)
rmw
34
2.9k
Large-scale JavaScript Application Architecture
addyosmani
511
110k
Transcript
Deep Learning and Natural Language Processing with Spark Melanie Warrick
| Skymind | @nyghtowl Andy Petrella | Data Fellas | @noootsab
@nyghtowl Machine Learning
None
@nyghtowl
@nyghtowl
Natural Language Processing @nyghtowl • Question Answer • Image Captioning
• Topic Modeling/Sentiment Analysis • Language | Machine Translation • Text Generation NLP is hard
@nyghtowl Artificial Neural Nets Output | y Hidden Loss Function
Output k j X M kj W y Run until error stops improving = converge Input | X
@nyghtowl Recurrent Neural Net
@nyghtowl Long short-term Memory (LSTM)
@nyghtowl Sequence to Sequence
@nyghtowl Example: Word2Vec Word embeddings represent context King – Man
+ Woman ~ Queen
@nyghtowl Example: Image Captioning
@nyghtowl Sequence to Sequence
@nyghtowl Sentiment Analysis Reviews “Best part of the movie is
the end credits” “It should have been a great movie…” Sentiment
@nyghtowl Hadoop Spark AWS Skymind ND4J DeepLearning4J Native & JavaCPP
& OpenMP & Cuda 7.5 Canova Data Neural Nets Linear Algebra LIBND4J C Backend
@nyghtowl Data Fellas - Spark-Notebook only Scala based notebook that
is - scalable and enables interactive work on Spark, Akka, Cassandra, & Kafka - plotting interactive plots in any Scala type - Data Fellas enables data-driven business, bringing productivity to data science in enterprise
@nyghtowl Cluster Juju bundle including: • DL4J • Mesos •
Spark • Spark Notebook
@nyghtowl Blog Making deep learning accessible on Openstack
@nyghtowl Research References RNNs • DL4J Overview: ◦ RNN &
LSTM Overview: http://deeplearning4j.org/recurrentnetwork ◦ Using RNNs: http://deeplearning4j.org/usingrnns.html • Karpathy: https://karpathy.github.io/2015/05/21/rnn-effectiveness/ • Intro: http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ Github Repos • Spark Notebook: https://github.com/andypetrella/spark-notebook • DL4J Examples: https://github.com/deeplearning4j/dl4j-0.4-examples • DL4J Spark Examples: https://github.com/deeplearning4j/dl4j-spark-cdh5-examples General ML Projects (referenced in presentation) • AlphaGo: http://i.dailymail.co.uk/i/pix/2016/03/09/09/320583D500000578-3483569- Google_has_confirmed_its_AlphaGo_computer_has_taken_the_first_vi-a-11_1457516282972.jpg • Switzerland SAR: http://www.forensicmag.com/article/2016/02/autonomous-drones-fly-search-and-rescue-operations • VIV: http://www.dailytech.com/ExSiri+CEO+Poaches+Apple+to+Create+Viv+The+Global+Brain/article36387.htm
@nyghtowl Image References • http://www.dailytech. com/ExSiri+CEO+Poaches+Apple+to+Create+Viv+The+Global+Brain/article36387.htm • http://3.bp.blogspot.com/- mMPT3tgVWaQ/U5qVs64HbRI/AAAAAAAAJCM/lEE4OiJmRSY/s1600/thumb-down-smiley.png •
http://4.bp.blogspot.com/-pUoO5oOuzOc/VcomU6qKT4I/AAAAAAAAAsg/TonkgL1iEjE/s1600/Screen% 2BShot%2B2015-08-11%2Bat%2B9.43.21%2BAM.png • http://www.ucreative.com/inspiration/interesting-patterns-and-fractals-from-nature/ • http://i.telegraph.co.uk/multimedia/archive/02122/WILLIAM-SHAKESPEAR_2122089b.jpg • https://karpathy.github.io/2015/05/21/rnn-effectiveness/ • https://pbs.twimg.com/media/CJm9HmfVEAEXU0c.jpg:large • http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ • http://i.dailymail.co.uk/i/pix/2016/03/09/09/320583D500000578-3483569- Google_has_confirmed_its_AlphaGo_computer_has_taken_the_first_vi-a-11_1457516282972.jpg • http://www.forensicmag.com/article/2016/02/autonomous-drones-fly-search-and-rescue-operations • Susan Eraly
@nyghtowl Deep Learning and Natural Language Processing with Spark Andy
Petrella | Data Fellas | @noootsab Melanie Warrick | Skymind | @nyghtowl