Personal Information
Organización/Lugar de trabajo
Greater Los Angeles Area, CA United States
Ocupación
Machine Learning guy / Data Scientist
Sector
Technology / Software / Internet
Acerca de
I am a seasoned DataScientist. My area of interests is Statistical / Machine Learning modeling( Bayesian and Frequentist Modeling techniques ). In my past life I have lead initiatives and worked on solving problems related to predicting pre-emptive measure to avoid failure for improving operating efficiency in Oil n Gas Industry, social media analysis, recommendation engines, match-making using statistical models, fraud-detection, natural language processing and others.
Currently, I am curious about how to efficiently understand the true nature of predictive models and that could lead to better testing and evaluation of the same.
Etiquetas
machine learning
analytics
big data
datascience
deep learning
statistics
bayesian learning
neural network
recommendation engine
uci
nlp
spark
optimization
Ver más
Presentaciones
(8)Recomendaciones
(38)Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!
Sri Ambati
•
Hace 5 años
Model Evaluation in the land of Deep Learning
Pramit Choudhary
•
Hace 5 años
IE: Named Entity Recognition (NER)
Marina Santini
•
Hace 8 años
Anomaly detection
QuantUniversity
•
Hace 7 años
Automatic Visualization - Leland Wilkinson, Chief Scientist, H2O.ai
Sri Ambati
•
Hace 6 años
Interpretable Machine Learning
Sri Ambati
•
Hace 5 años
Interpretable machine learning
Sri Ambati
•
Hace 7 años
Making Netflix Machine Learning Algorithms Reliable
Justin Basilico
•
Hace 6 años
Learning to learn - to retrieve information
Pramit Choudhary
•
Hace 6 años
Model evaluation in the land of deep learning
Pramit Choudhary
•
Hace 5 años
Production and Beyond: Deploying and Managing Machine Learning Models
Turi, Inc.
•
Hace 8 años
Icml2012 tutorial representation_learning
zukun
•
Hace 11 años
Is that a Time Machine? Some Design Patterns for Real World Machine Learning Systems
Justin Basilico
•
Hace 7 años
Data Workflows for Machine Learning - Seattle DAML
Paco Nathan
•
Hace 10 años
Lessons Learned from Building Machine Learning Software at Netflix
Justin Basilico
•
Hace 9 años
Apache Spark Model Deployment
Databricks
•
Hace 7 años
Convolutional Neural Networks (CNN)
Gaurav Mittal
•
Hace 8 años
To explain or to predict
Galit Shmueli
•
Hace 11 años
10 Lessons Learned from Building Machine Learning Systems
Xavier Amatriain
•
Hace 9 años
Recommendations for Building Machine Learning Software
Justin Basilico
•
Hace 7 años
Improving Python and Spark Performance and Interoperability: Spark Summit East talk by: Wes McKinney
Spark Summit
•
Hace 7 años
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East talk by DB Tsai
Spark Summit
•
Hace 7 años
Uber's data science workbench
Ran Wei
•
Hace 7 años
Interpreting machine learning models
andosa
•
Hace 8 años
Strata 2014 Anomaly Detection
Ted Dunning
•
Hace 10 años
Deploying ml
Turi, Inc.
•
Hace 9 años
Monte Carlo Simulations in Ad-Lift Measurement Using Spark by Prasad Chalasani and Ram Sriharsha
Spark Summit
•
Hace 8 años
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and Scala
Helena Edelson
•
Hace 9 años
Parallel and Iterative Processing for Machine Learning Recommendations with Spark
MapR Technologies
•
Hace 8 años
Personal Information
Organización/Lugar de trabajo
Greater Los Angeles Area, CA United States
Ocupación
Machine Learning guy / Data Scientist
Sector
Technology / Software / Internet
Acerca de
I am a seasoned DataScientist. My area of interests is Statistical / Machine Learning modeling( Bayesian and Frequentist Modeling techniques ). In my past life I have lead initiatives and worked on solving problems related to predicting pre-emptive measure to avoid failure for improving operating efficiency in Oil n Gas Industry, social media analysis, recommendation engines, match-making using statistical models, fraud-detection, natural language processing and others.
Currently, I am curious about how to efficiently understand the true nature of predictive models and that could lead to better testing and evaluation of the same.
Etiquetas
machine learning
analytics
big data
datascience
deep learning
statistics
bayesian learning
neural network
recommendation engine
uci
nlp
spark
optimization
Ver más