Active learning from streams of graph, language & time series signals
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The document discusses active learning from streams of graph, language, and time series data. It introduces why active learning and feedback are important for analyzing large and continuous streams of data from sources like emails, account transactions, and social networks. It also discusses using a hybrid of rules, time series analysis, link analysis, natural language processing, and ensemble machine learning to iteratively analyze user data from multiple streams and improve results through agent feedback.
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Pattern mining is an unsupervised machine learning technique used to discover frequent patterns and relationships in log data. It involves finding the top frequent sets of items that occur together in the data at least a minimum number of times. There are two main approaches - candidate generation which generates and filters candidate patterns in multiple passes over the data, and pattern growth which constructs conditional databases to avoid multiple full scans. Pattern mining can be used to find commonly purchased itemsets, extract features from log data, and derive rules for recommendations.
Intelligent Applications with Machine Learning Toolkits
Shawn Scully from Dato discusses how their machine learning toolkits can help developers quickly build intelligent applications. Their toolkits provide pre-built models for common tasks like recommendation, sentiment analysis, similarity search, churn prediction, and data matching. Developers can easily create applications with just a few lines of code, deploy models as microservices, and iteratively improve applications based on feedback. Dato aims to accelerate innovators by providing agile machine learning tools.
Text Analysis with Machine Learning
The document discusses text analysis with machine learning. It begins with introductions and then covers applications of text analysis like product reviews and social media. The bulk of the document discusses fundamentals of text processing like tokenization and feature engineering. It also discusses machine learning toolkits and task-oriented tools like sentiment analysis. Advanced topics like topic models and word embeddings are briefly introduced. The presentation aims to provide an overview of text analysis and point to further resources.
Machine Learning with GraphLab Create
This document introduces Dato and its machine learning platform. Dato provides intuitive APIs and toolkits that allow developers to easily create intelligent applications for tasks like recommendation, sentiment analysis, churn prediction, and more. It offers scalable data structures, high performance algorithms, and the ability to quickly develop and deploy machine learning models and services. Customers across various industries have been able to build and operationalize intelligent solutions faster using Dato to solve problems in fraud detection, data matching, recommendations, and other domains.
Machine Learning in Production with Dato Predictive Services
The document discusses Dato Predictive Services, a machine learning platform that helps deploy, serve, monitor, and manage machine learning models in production. It provides an overview of key capabilities like deploying models through different options, monitoring model performance and product usage, and evaluating models with online experiments. These capabilities aim to address common challenges of machine learning in production like deploying trained models, monitoring their behavior, and continuously improving them. The presentation includes a demo of a book recommender application built with Dato Predictive Services.
Machine Learning in 2016: Live Q&A with Carlos Guestrin
Live webinar session with Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. Carlos reviewed 2015 highlights, previewed the Dato roadmap, and answered real-time questions from participants about use cases, algorithms, and resources.
Scalable data structures for data science
This document discusses scalable out-of-core data structures for data science. It introduces SFrame and SGraph, which allow machine learning on large datasets that exceed memory by using compressed columnar storage and lazy evaluation. SFrame provides a Python API for feature engineering and vectorized operations on tabular data. SGraph supports graph algorithms like PageRank on very large graphs with billions of nodes and edges. These tools are open source and support HDFS, S3, and other storage backends to enable scalable machine learning.
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Overview of Deep Learning for Image Analysis, including Convolutional Neural Nets and Deep Features.
Introduction to Recommender Systems
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of recommender systems. Learn foundations (e.g., collaborative filtering, content-based methods, hybrid methods) and get practical tips for putting models into production.
Machine learning in production
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
Overview of Machine Learning and Feature Engineering
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.
SFrame
Scalable tabular (SFrame, SArray) and graph (SGraph) data-structures built for out-of-core data analysis.
The SFrame package provides the complete implementation of:
SFrame
SArray
SGraph
The C++ SDK surface area (gl_sframe, gl_sarray, gl_sgraph)
Building Personalized Data Products with Dato
This document discusses building personalized data products and recommender systems using implicit and explicit user data. It describes how recommender systems work by using matrix factorization to learn latent factors about users and items from interaction data in order to predict ratings and rankings to drive personalized recommendations. The document also notes that recommender systems are commonly used by Netflix, Spotify, LinkedIn and Facebook to power personalized experiences and that even small improvements in recommendation quality can lead to significant business value.
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Webinar - Pattern Mining Log Data - Vega (20160426)
Webinar - Pattern Mining Log Data - Vega (20160426)
Scaling Up Machine Learning: How to Benchmark GraphLab Create on Huge Datasets
Scaling Up Machine Learning: How to Benchmark GraphLab Create on Huge Datasets
Intelligent Applications with Machine Learning Toolkits
Intelligent Applications with Machine Learning Toolkits
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Machine Learning in Production with Dato Predictive Services
Machine Learning in 2016: Live Q&A with Carlos Guestrin
Machine Learning in 2016: Live Q&A with Carlos Guestrin
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Overview of Machine Learning and Feature Engineering
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Demystifying Knowledge Management through Storytelling
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- Understand the value of KM and the benefits of engaging
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- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Cyber Recovery Wargame
For senior executives, successfully managing a major cyber attack relies on your ability to minimise operational downtime, revenue loss and reputational damage.
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Event date: 19th June 2024, Tate Modern
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Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
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Interoperability at its Core
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Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
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Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
This LF Energy webinar took place June 20, 2024. It featured:
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-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
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Mutation Testing for Task-Oriented Chatbots
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Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
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We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
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Must Know Postgres Extension for DBA and Developer during Migration
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
QA or the Highway - Component Testing: Bridging the gap between frontend appl...
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Guidelines for Effective Data Visualization
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"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
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Demystifying Knowledge Management through Storytelling
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LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
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Essentials of Automations: Exploring Attributes & Automation Parameters
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Must Know Postgres Extension for DBA and Developer during Migration
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"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
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Active learning from streams of graph, language & time series signals
- 1. Active learning from streams of graph, language & time series signals David Talby SVP Engineering Atigeo Claudiu Branzan Senior Engineering Lead Atigeo
- 2. Why Active Learning & Feedback? 50+Schemes (and counting) 99.9999 %‘Good’ messages 6+Months per case
- 3. Why Hybrid Analytics? Ignore more rules Unusual timing of eventsUnusual personal network Teamwork & scale Think & talk differently
- 4. Why Hybrid Analytics? Rules & Policies Time Series Analysi sLink Analysi s Ensembl e Learnin g Natural Language
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- 6. User Analysis Iteration Email NLP Features User graph Transactio ns time series Graph Features Time Series Features NLP Features Agent Feedback Train/TestClassifier
- 7. 7 Thank You! Notebooks for this talk are freely available David.Talby@atigeo.com Claudiu.Branzan@atigeo.com Try xPatterns Connect at: http://xpatterns.com/connect/