Personal Information
Entreprise/Lieu de travail
Sebastopol, CA United States
Profession
Evil Mad Scientist
Secteur d’activité
Technology / Software / Internet
Site Web
derwen.ai/paco
À propos
Known as a "player/coach", with core expertise in data science, natural language processing, machine learning, cloud computing; 35+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Co-chair Rev. Advisor for Amplify Partners, Deep Learning Analytics, Primer, Data Spartan, Recognai. Recent roles: Director, Learning Group @ O'Reilly Media; Director, Community Evangelism @ Databricks and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.
Mots-clés
big data
data science
machine learning
hadoop
cascading
spark
mesos
scalding
cascalog
nlp
python
jupyter
scala
use cases
enterprise data workflows
ai
textrank
streaming
twitter
cluster computing
open data
pmml
aws
cloud computing
text analytics
r
active learning
graph algorithms
approximation algorithms
case studies
ipython notebook
functional programming
management
human-in-the-loop
learning
docker
mesosphere
clojure
o'reilly media
publishing
real-time analytics
sql
knime
advanced math
distributed systems
google
predictive modeling
java
disambiguation
ontology
open source
scikit-learn
chicago
history
apache hadoop
analytics
networkx
datasketch
spacy
deep learning
content discovery
media
video
computable content
inverted classroom
education
graphx
community
certification
mooc
graph queries
abstract algebra
datacenter computing
marathon
linux
low latency
graph theory
airbnb
linux containers
isolation
borg
mathematics
statistics
portland
sas
ansi sql
palo alto
mapreduce
algorithms
enterprise
redis
gephi
business strategy
social media
knowledge graph
search
learning experiences
nike
nginx
kaltura
best practices
literate programming
summarization
standards
pfa
accountability
governance
avro
recommender systems
social context
kubernetes
learning curve
continuous learning
computational thinking
philosophy
parquet
thebe
json
oscon
notebooks
brazil
sao paulo
qcon
iot
paco nathan
pagerank
probabilistic data structures
system architecture
business
stanford
functio
cluster scheduling
quasar
probabilistic programming
chronos
cgroups
omega
mbrace
augustus
julia
mlbase
summingbird
titan
genetic programming
metascale
sears
chug
virtualization
university of chicago
ensembles
kdd
hadoop summit
windows azure
texas
pattern language
predictive models
optimization
tdd
optiq
application layer
enterprise architecture
splunk
bigdata
tf-idf
data analysis
pentaho
imvu
continuous deployment
emr
enron
infochimps
datameer
Tout plus
Présentations
(73)J’aime
(111)When Privacy Scales - Intelligent Product Design under GDPR
Amanda Casari
•
il y a 5 ans
Data science apps powered by Jupyter Notebooks
Natalino Busa
•
il y a 6 ans
Learning to learn Model Behavior: How to use "human-in-the-loop" to explain decisions.
IDEAS - Int'l Data Engineering and Science Association
•
il y a 6 ans
Data Science with Human in the Loop @Faculty of Science #Leiden University
Lora Aroyo
•
il y a 7 ans
Making fashion recommendations with human-in-the-loop machine learning
Brad Klingenberg
•
il y a 7 ans
Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention
DataWorks Summit
•
il y a 6 ans
Active Learning and Human-in-the-Loop
CrowdFlower
•
il y a 7 ans
Managing and Versioning Machine Learning Models in Python
Simon Frid
•
il y a 7 ans
WTF - Why the Future Is Up to Us - pptx version
Tim O'Reilly
•
il y a 7 ans
Container Ship - How to reduce effect on Climate and Pollution
Glenn Klith Andersen
•
il y a 15 ans
SKIL - Dl4j in the wild meetup
Adam Gibson
•
il y a 7 ans
Finding Key Influencers and Viral Topics in Twitter Networks Related to ISIS, Brexit, and the 2016 Elections
Steve Kramer
•
il y a 7 ans
Anomaly Detection in Deep Learning (Updated)
Adam Gibson
•
il y a 7 ans
AnalyticOps: Lessons Learned Moving Machine-Learning Algorithms to Production Environments
Robert Grossman
•
il y a 8 ans
Non-exhaustive, Overlapping K-means
David Gleich
•
il y a 8 ans
Dimensionality Reduction of Genomic Variation with Big Data Genomics ADAM & Spark MLLib/ML & SparkR
Deborah Siegel
•
il y a 8 ans
Lecture 1 introduction To The Course: The Flipped Classroom
Marina Santini
•
il y a 9 ans
Designing Reactive Systems with Akka
Thomas Lockney
•
il y a 8 ans
Sparkling pandas Letting Pandas Roam - PyData Seattle 2015
Holden Karau
•
il y a 8 ans
How to Hire Data Scientists
Galvanize
•
il y a 8 ans
Spark meetup london share and analyse genomic data at scale with spark, adam, tachyon and the spark notebook
Andy Petrella
•
il y a 8 ans
Spark Meetup @ Netflix, 05/19/2015
Yves Raimond
•
il y a 8 ans
Distributed machine learning 101 using apache spark from the browser
Andy Petrella
•
il y a 8 ans
Spark Summit 2015 Highlights in Tweets
Gerard Maas
•
il y a 8 ans
Hadoop Summit 2015: Performance Optimization at Scale, Lessons Learned at Twitter (Alex Levenson)
Alex Levenson
•
il y a 8 ans
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, Scala
Helena Edelson
•
il y a 8 ans
Apache spark meetup
Israel Gaytan
•
il y a 9 ans
Scala Days San Francisco
Martin Odersky
•
il y a 9 ans
Building and Deploying Application to Apache Mesos
Joe Stein
•
il y a 9 ans
Personal Information
Entreprise/Lieu de travail
Sebastopol, CA United States
Profession
Evil Mad Scientist
Secteur d’activité
Technology / Software / Internet
Site Web
derwen.ai/paco
À propos
Known as a "player/coach", with core expertise in data science, natural language processing, machine learning, cloud computing; 35+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Co-chair Rev. Advisor for Amplify Partners, Deep Learning Analytics, Primer, Data Spartan, Recognai. Recent roles: Director, Learning Group @ O'Reilly Media; Director, Community Evangelism @ Databricks and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.
Mots-clés
big data
data science
machine learning
hadoop
cascading
spark
mesos
scalding
cascalog
nlp
python
jupyter
scala
use cases
enterprise data workflows
ai
textrank
streaming
twitter
cluster computing
open data
pmml
aws
cloud computing
text analytics
r
active learning
graph algorithms
approximation algorithms
case studies
ipython notebook
functional programming
management
human-in-the-loop
learning
docker
mesosphere
clojure
o'reilly media
publishing
real-time analytics
sql
knime
advanced math
distributed systems
google
predictive modeling
java
disambiguation
ontology
open source
scikit-learn
chicago
history
apache hadoop
analytics
networkx
datasketch
spacy
deep learning
content discovery
media
video
computable content
inverted classroom
education
graphx
community
certification
mooc
graph queries
abstract algebra
datacenter computing
marathon
linux
low latency
graph theory
airbnb
linux containers
isolation
borg
mathematics
statistics
portland
sas
ansi sql
palo alto
mapreduce
algorithms
enterprise
redis
gephi
business strategy
social media
knowledge graph
search
learning experiences
nike
nginx
kaltura
best practices
literate programming
summarization
standards
pfa
accountability
governance
avro
recommender systems
social context
kubernetes
learning curve
continuous learning
computational thinking
philosophy
parquet
thebe
json
oscon
notebooks
brazil
sao paulo
qcon
iot
paco nathan
pagerank
probabilistic data structures
system architecture
business
stanford
functio
cluster scheduling
quasar
probabilistic programming
chronos
cgroups
omega
mbrace
augustus
julia
mlbase
summingbird
titan
genetic programming
metascale
sears
chug
virtualization
university of chicago
ensembles
kdd
hadoop summit
windows azure
texas
pattern language
predictive models
optimization
tdd
optiq
application layer
enterprise architecture
splunk
bigdata
tf-idf
data analysis
pentaho
imvu
continuous deployment
emr
enron
infochimps
datameer
Tout plus