Personal Information
Unternehmen/Arbeitsplatz
Greater Los Angeles Area, CA United States
Beruf
Machine Learning guy / Data Scientist
Branche
Technology / Software / Internet
Info
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.
Tags
machine learning
analytics
big data
datascience
deep learning
statistics
bayesian learning
neural network
recommendation engine
uci
nlp
spark
optimization
Mehr anzeigen
Präsentationen
(8)Gefällt mir
(38)Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!
Sri Ambati
•
Vor 5 Jahren
Model Evaluation in the land of Deep Learning
Pramit Choudhary
•
Vor 5 Jahren
IE: Named Entity Recognition (NER)
Marina Santini
•
Vor 8 Jahren
Anomaly detection
QuantUniversity
•
Vor 7 Jahren
Automatic Visualization - Leland Wilkinson, Chief Scientist, H2O.ai
Sri Ambati
•
Vor 6 Jahren
Interpretable Machine Learning
Sri Ambati
•
Vor 5 Jahren
Interpretable machine learning
Sri Ambati
•
Vor 7 Jahren
Making Netflix Machine Learning Algorithms Reliable
Justin Basilico
•
Vor 6 Jahren
Learning to learn - to retrieve information
Pramit Choudhary
•
Vor 6 Jahren
Model evaluation in the land of deep learning
Pramit Choudhary
•
Vor 5 Jahren
Production and Beyond: Deploying and Managing Machine Learning Models
Turi, Inc.
•
Vor 8 Jahren
Icml2012 tutorial representation_learning
zukun
•
Vor 11 Jahren
Is that a Time Machine? Some Design Patterns for Real World Machine Learning Systems
Justin Basilico
•
Vor 7 Jahren
Data Workflows for Machine Learning - Seattle DAML
Paco Nathan
•
Vor 10 Jahren
Lessons Learned from Building Machine Learning Software at Netflix
Justin Basilico
•
Vor 9 Jahren
Apache Spark Model Deployment
Databricks
•
Vor 7 Jahren
Convolutional Neural Networks (CNN)
Gaurav Mittal
•
Vor 8 Jahren
To explain or to predict
Galit Shmueli
•
Vor 11 Jahren
10 Lessons Learned from Building Machine Learning Systems
Xavier Amatriain
•
Vor 9 Jahren
Recommendations for Building Machine Learning Software
Justin Basilico
•
Vor 7 Jahren
Improving Python and Spark Performance and Interoperability: Spark Summit East talk by: Wes McKinney
Spark Summit
•
Vor 7 Jahren
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East talk by DB Tsai
Spark Summit
•
Vor 7 Jahren
Uber's data science workbench
Ran Wei
•
Vor 7 Jahren
Interpreting machine learning models
andosa
•
Vor 8 Jahren
Strata 2014 Anomaly Detection
Ted Dunning
•
Vor 10 Jahren
Deploying ml
Turi, Inc.
•
Vor 9 Jahren
Monte Carlo Simulations in Ad-Lift Measurement Using Spark by Prasad Chalasani and Ram Sriharsha
Spark Summit
•
Vor 8 Jahren
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and Scala
Helena Edelson
•
Vor 9 Jahren
Parallel and Iterative Processing for Machine Learning Recommendations with Spark
MapR Technologies
•
Vor 8 Jahren
Personal Information
Unternehmen/Arbeitsplatz
Greater Los Angeles Area, CA United States
Beruf
Machine Learning guy / Data Scientist
Branche
Technology / Software / Internet
Info
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.
Tags
machine learning
analytics
big data
datascience
deep learning
statistics
bayesian learning
neural network
recommendation engine
uci
nlp
spark
optimization
Mehr anzeigen