Sharing a study case from Kaggle competition, Facebook V: Predicting Check Ins data science competition. Hope will bring R users more possibilities using R doing Kaggle competition!
For community sharing usage.
The document reports on the author's experience at the Agile 2009 conference in Chicago. It details the speaker sessions and activities each day of the conference, including keynote speeches, open jam sessions on topics like user story mapping and sketching & prototyping, and games like the Kanban game. The document concludes with acknowledgements and notes that Agile conferences focus on Agile principles and user-centered design.
A Gentle Introduction to Tidy Statistics in R.pdfVickyAlers
?
this can be found in rstudio website. Description from the website: A Gentle Introduction to Tidy Statistics in R
R is a fantastic language for statistical programming, but making the jump from point and click interfaces to code can be intimidating for individuals new to R.
2019-06-12
My presentation from RedDotRubyConf 2013 in Singapore. Turned out to be a reflection on whether I'd still be a Rubyist in another 5 years, and what are the external trends that might change that. Short story: Yes! Of course. I'll always think like a Rubyist even though things will probably get more polyglot. The arena of web development is perhaps the most unpredictable though.
Scaling with apache spark (a lesson in unintended consequences) strange loo...Holden Karau
?
This document discusses scaling Apache Spark applications and some of the unintended consequences that can arise. It covers Spark's core abstractions of RDDs and DataFrames for distributed data and computation. It explains how Spark's lazy evaluation model and use of deterministic partitioning can impact reusing data and operations like groupByKey. It also discusses challenges that can arise from Spark's support for arbitrary functions and working with non-JVM languages like Python.
Sharing (or stealing) the jewels of python with big data & the jvm (1)Holden Karau
?
With the new Apache Arrow integration in PySpark 2.3, it is now starting become reasonable to look to the Python world and ask “what else do we want to steal besides tensorflow”, or as a Python developer look and say “how can I get my code into production without it being rewritten into a mess of Java?”
Regardless of your specific side(s) in the JVM/Python divide, collaboration is getting a lot faster, so lets learn how to share! In this brief talk we will examine sharing some of the wonders of Spacy with the Java world, which still has a somewhat lackluster set of options for NLP.
The document discusses Rosetta Code, an online collection of programming tasks solved in many different languages, and encourages attendees to contribute R solutions to unsolved tasks or improve existing solutions. It also reviews some existing R code on Rosetta Code to discuss idiomatic R style and seeks help with solving the task of finding Pythagorean triples with a given diameter in a functional way.
This document discusses Padrino, a web framework built on Sinatra that aims to provide a structured and flexible structure. Padrino allows developers to choose different options for the ORM, testing framework, JavaScript library, template engine, and stylesheet to use. It has generators to quickly scaffold a project with the chosen options. The philosophy is to keep things simple to use and hack while giving developers freedom. Major features include being agnostic to different options, generators for scaffolding projects, mounting multiple apps, and an admin interface.
Introduction to Python Syntax and SemanticsAdam Cook
?
This is the slide deck from the first webinar or our chapter's (SME Chapter 112) "Python for Engineers and Manufacturers" series. The webinar was held on July 27, 2017.
All of the slide decks and code for this webinar series are located at: https://github.com/sme112/python_webinars
To learn about SME Chapter 112 and our events, please visit the following links:
https://www.facebook.com/sme112/
https://www.linkedin.com/company/sme112
This document compares Python MapReduce and Scalding, two frameworks for running MapReduce jobs on Hadoop. It provides an overview of each tool, examples of word count jobs in Python MapReduce and Scalding, and compares the pros and cons of each approach. The author works as a data engineer at Spotify and provides their background and contact information.
The document discusses projects for working with Resource Description Framework (RDF) data in the Hadoop ecosystem. It describes Apache Jena Elephas, a set of modules that enable RDF on Hadoop by providing Writable types for RDF primitives and input/output support. It also discusses Intel Graph Builder, which allows graphs to be created or transformed from data sources using Apache Pig. The document encourages participants to try out these projects and contribute by suggesting features, reporting issues, or contributing code.
A look at the changing perceptions of R, from the early days of the R project to today. Microsoft sponsor talk, presented by David Smith to the useR!2017 conference in Brussels, July 5 2017.
Greach es el evento sobre tecnologías basadas en lenguaje Groovy referente en Espa?a.
Dentro de este evento, la charla 'Use Groovy & Grails in your Spring Boot projects' se presenta como una propuesta de ejemplos y posibilidades de introducir este lenguaje y algunos módulos del framework Grails (basado también en Groovy) en proyectos implementados con la reciente solución lanzada por Spring llama Spring Boot.
More info:
http://buff.ly/1DXXQWU
PyData Frankfurt - (Efficient) Data Exchange with "Foreign" EcosystemsUwe Korn
?
As a Data Scientist/Engineer in Python, we focus in our work to solve problems with large amounts of data but still stay in Python. This is where we are the most effective and feel comfortable. Libraries like Pandas and NumPy provide us with efficient interfaces to deal with this data while still getting optimal performance. The main problem appears when we have to deal with systems outside of our comfort ecosystem. We need to write cumbersome and mostly slow conversion code that ingests data from there into our pipeline until we can work efficiently. Using Apache Arrow and Parquet as base technologies, we get a set of tools that eases this interaction and also brings us a huge performance improvement. As part of the talk we will show a basic problem where we take data coming from a Java application through Python into using these tools.
PyData: Past, Present Future (PyData SV 2014 Keynote)Peter Wang
?
From the closing keynoteLook back at the last two years of PyData, discussion about Python's role in the growing and changing data analytics landscape, and encouragement of ways to grow the community
Many of the recent big data systems, like Hadoop, Spark, and Kafka, are written primarily in JVM languages. At the same time, there is a wealth of tools for data science and data analytics that exist outside of the JVM. Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).
Holden and Rachel detail how to bridge the gap using PySpark and discuss other solutions like Kafka Streams as well. They also outline the challenges of pure Python solutions like dask. Holden and Rachel start with the current architecture of PySpark and its evolution. They then turn to the future, covering Arrow-accelerated interchange for Python functions, how to expose Python machine learning models into Spark, and how to use systems like Spark to accelerate training of traditional Python models. They also dive into what other similar systems are doing as well as what the options are for (almost) completely ignoring the JVM in the big data space.
Python users will learn how to more effectively use systems like Spark and understand how the design is changing. JVM developers will gain an understanding of how to Python code from data scientist and Python developers while avoiding the traditional trap of needing to rewrite everything.
[DevRelCon Tokyo 2017] Creative Technical Content for Better Developer Experi...Tomomi Imura
?
Let’s say, you are searching certain frameworks, or APIs to satisfy your new project- what if you stumble on some awesome-sounding shiny website, but it comes with very poor documentations. Do you want to try it out, or keep searching something else? Or when you see a GitHub project with no README, how do you feel? I think this developer experience is one of big key factors for you to decide what technologies to use.
User-Experience (UX) focuses on understanding what users' need and value, and provide practical products or services. This human-computer interaction acts the same when the users are developers. The ideas of “Developer Experiences” is to establish a good relationship between developers and platform providers.
So, as a developer evangelist, what can we do to improve DX to get developers' interests?
In this talk, Tomomi Imura will talk about her experiences, and how I create developer-centric contents and docs to drive the community and acquired new developers and customers.
Machine vision and device integration with the Ruby programming language (2008)Jan Wedekind
?
This document provides a summary of a research seminar on machine vision and device integration using the Ruby programming language. The seminar will be held on February 29th, 2008 and will discuss projects using a transmission electron microscope, digital camera, piezo controller and nano indenter as well as a micro camera and piezo drives. It will also discuss proprietary business models versus community development models and differences between GPLv3 and BSD licenses. An introduction to the Ruby programming language is provided including statistics on usage from the Tiobe index and speed comparisons to other languages.
Improving Enterprise Agility via a Lean LensAndrea Darabos
?
The document discusses using lean principles to improve enterprise agility in software development. It defines waste as anything that does not add value from the customer's perspective in either products or processes. Eight common types of waste in software development are identified, including partially completed work and defects. The document proposes a lean lens game to help development teams identify waste in their processes by acting out scenarios and mapping out value streams and waste. Practical takeaways include using value stream mapping and discussing waste regularly in retrospectives to continuously improve.
Puppet Camp Dallas 2014: How Puppet Ops RollsPuppet
?
The document discusses how Puppet Ops manages its Puppet infrastructure. It recommends using good modules from the Forge or GitHub instead of reinventing the wheel. Roles and profiles are used to logically organize Puppet code and consume modules. Hiera drives Puppet configuration with data and allows grouping data by environment. R10k and dynamic environments help keep code agile for multiple developers. Useful tools mentioned include Puppet Query, Puppet Dashboard, Puppet Lint, and Beaker for testing. Hardware recommendations include using a properly sized server to avoid Puppet performance issues.
Evaluating Large Language Models for Your Applications and Why It MattersMia Chang
?
Event: AWS WUG Cloud Talks
Date: 2025-02-11
Description: Confused by the overwhelming metrics for evaluating LLMs? This talk will guide you through key evaluation metrics, tools, and frameworks tailored to specific use cases, including mitigating social biases and extracting interpretable features. Gain clarity on LLM evaluation to build better generative AI applications.
Service: Amazon Bedrock
Speaker: Mia Chang: ML Specialist Solutions Architect at AWS, NLP expert, and author, with extensive experience in AI/ML workloads on the cloud.
Running the first automatic speech recognition (ASR) model with HuggingFace -...Mia Chang
?
Running the first automatic speech recognition (ASR) model with HuggingFace
06-18, 11:00–11:45 (Europe/London), Tower Suite 1
Come and learn your first audio machine learning model with Automatic speech recognition (ASR) use case! ASR has been a popular application like voice-controlled assistants and voice-to-text/speech-to-text applications. These applications take audio clips as input and convert speech signals to text.
This talk is aiming for Python developers or ML practitioners who are knowing Python, and interested in working with audio machine learning use case. I will cover minimum slides about ML algorithm in this talk. Instead, I will walk through types of ASR applications, like automatic subtitling for videos and transcribing meetings. So you will know what are the occasions to work with ASR models. And talk about data processing of audio data, how to do feature extraction, and Fine-tune Wav2Vec2 using HuggingFace. The notebook that presented in the talk is running on Amazon SageMaker, the concept for this talk is cloud agnostic and applies to local computer(on premises) as well.
---
Github: https://github.com/pymia/amazon-sagemaker-fine-tune-and-deploy-wav2vec2-huggingface
Event: PyData London 2022
Date: JUNE 17TH-19TH, 2022
Event link: https://pydata.org/london2022/
Linkedin: http://linkedin.com/in/mia-chang/
7 steps to AI production - global azure bootcamp 2020 KolnMia Chang
?
Session: 7 steps to AI production
Abstract: What was your last AI project? Was it another Kaggle dataset running on Jupyter notebook, hard to reproduce, and don't know how to deploy as an AI service? How to do auto-scaling for the model serving?
How far is the distance from playing with the sample dataset to AI production?
Let's go through 7 steps in the AI application development lifecycle. From data wrangling, reproduce your training, model acceptance to model deployment and management.
Target audience: Data scientist who doesn't know the model serving and Azure DevOps. Backend/DevOps who doesn't know how to help your data team go production.
---
Github: https://github.com/pymia/7-steps-production
Event: Global Azure Bootcamp 2020 Virtual
Date: Apr 25, 2020
Event link: https://www.meetup.com/Azure-Cologne-Meetup/events/266727986/
Linkedin: http://linkedin.com/in/mia-chang/
The content was modified from Google Content Group
Eric ShangKuan(ericsk@google.com)
---
TensorFlow Lite guide( for mobile & IoT )
TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and small binary size.
TensorFlow Lite consists of two main components:
The TensorFlow Lite interpreter:
- optimize models on many different hardware types, like mobile phones, embedded Linux devices, and microcontrollers.
The TensorFlow Lite converter:
- which converts TensorFlow models into an efficient form for use by the interpreter, and can introduce optimizations to improve binary size and performance.
---
Event: PyLadies TensorFlow All-Around
Date: Sep 25, 2019
Event link: https://www.meetup.com/PyLadies-Berlin/events/264205538/
Linkedin: http://linkedin.com/in/mia-chang/
DPS2019 data scientist in the real estate industry Mia Chang
?
This document summarizes a presentation about applying artificial intelligence in the real estate industry. It discusses the different stages of the real estate process and how AI could be used at each stage, including predicting energy usage, processing text in different languages, and automating workflows. It also covers challenges around regulations like GDPR and strategies for developing and deploying AI models, including using transfer learning and version control systems.
Leverage the power of machine learning on windowsMia Chang
?
Note:
The Content was modified from the Microsoft Content team.
Deck Owner: Nitah Onsongo
Tech/Msg Review: Cesar De La Torre, Simon Tao, Clarke Rahrig
---
Event: Insider Dev Tour Berlin
Event Description: Microsoft is going on a world tour with the announcements of Build 2019. The Insider Dev Tour focuses on innovations related to Microsoft 365 from a developer's perspective.
Date: June 7th, 2019
Event link: https://www.microsoft.com/de-de/techwiese/news/best-of-build-insider-dev-tour-am-7-juni-in-berlin.aspx
Linkedin: http://linkedin.com/in/mia-chang/
Develop computer vision applications with azure computer vision apiMia Chang
?
This document discusses developing computer vision applications using the Azure Computer Vision API. It provides an overview of computer vision and AI development on Azure. It also discusses using emotion recognition in chatbots and provides references to computer vision papers, datasets, and tools like the Azure Machine Learning Workbench. The document includes examples of computer vision tasks like object detection and segmentation and provides a small demo of emotion detection.
This document summarizes chapters 5 and 6 from a book on unit testing. Chapter 5 discusses why isolation frameworks are useful for creating fake objects more easily than hand-coding mocks. It also covers simulating fake values and testing events. Chapter 6 distinguishes between constrained and unconstrained isolation frameworks and discusses features that support future-proofing and usability of frameworks. Both chapters emphasize that isolation frameworks make testing easier, faster and less error-prone compared to manually writing mocks.
Introduction to Python Syntax and SemanticsAdam Cook
?
This is the slide deck from the first webinar or our chapter's (SME Chapter 112) "Python for Engineers and Manufacturers" series. The webinar was held on July 27, 2017.
All of the slide decks and code for this webinar series are located at: https://github.com/sme112/python_webinars
To learn about SME Chapter 112 and our events, please visit the following links:
https://www.facebook.com/sme112/
https://www.linkedin.com/company/sme112
This document compares Python MapReduce and Scalding, two frameworks for running MapReduce jobs on Hadoop. It provides an overview of each tool, examples of word count jobs in Python MapReduce and Scalding, and compares the pros and cons of each approach. The author works as a data engineer at Spotify and provides their background and contact information.
The document discusses projects for working with Resource Description Framework (RDF) data in the Hadoop ecosystem. It describes Apache Jena Elephas, a set of modules that enable RDF on Hadoop by providing Writable types for RDF primitives and input/output support. It also discusses Intel Graph Builder, which allows graphs to be created or transformed from data sources using Apache Pig. The document encourages participants to try out these projects and contribute by suggesting features, reporting issues, or contributing code.
A look at the changing perceptions of R, from the early days of the R project to today. Microsoft sponsor talk, presented by David Smith to the useR!2017 conference in Brussels, July 5 2017.
Greach es el evento sobre tecnologías basadas en lenguaje Groovy referente en Espa?a.
Dentro de este evento, la charla 'Use Groovy & Grails in your Spring Boot projects' se presenta como una propuesta de ejemplos y posibilidades de introducir este lenguaje y algunos módulos del framework Grails (basado también en Groovy) en proyectos implementados con la reciente solución lanzada por Spring llama Spring Boot.
More info:
http://buff.ly/1DXXQWU
PyData Frankfurt - (Efficient) Data Exchange with "Foreign" EcosystemsUwe Korn
?
As a Data Scientist/Engineer in Python, we focus in our work to solve problems with large amounts of data but still stay in Python. This is where we are the most effective and feel comfortable. Libraries like Pandas and NumPy provide us with efficient interfaces to deal with this data while still getting optimal performance. The main problem appears when we have to deal with systems outside of our comfort ecosystem. We need to write cumbersome and mostly slow conversion code that ingests data from there into our pipeline until we can work efficiently. Using Apache Arrow and Parquet as base technologies, we get a set of tools that eases this interaction and also brings us a huge performance improvement. As part of the talk we will show a basic problem where we take data coming from a Java application through Python into using these tools.
PyData: Past, Present Future (PyData SV 2014 Keynote)Peter Wang
?
From the closing keynoteLook back at the last two years of PyData, discussion about Python's role in the growing and changing data analytics landscape, and encouragement of ways to grow the community
Many of the recent big data systems, like Hadoop, Spark, and Kafka, are written primarily in JVM languages. At the same time, there is a wealth of tools for data science and data analytics that exist outside of the JVM. Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).
Holden and Rachel detail how to bridge the gap using PySpark and discuss other solutions like Kafka Streams as well. They also outline the challenges of pure Python solutions like dask. Holden and Rachel start with the current architecture of PySpark and its evolution. They then turn to the future, covering Arrow-accelerated interchange for Python functions, how to expose Python machine learning models into Spark, and how to use systems like Spark to accelerate training of traditional Python models. They also dive into what other similar systems are doing as well as what the options are for (almost) completely ignoring the JVM in the big data space.
Python users will learn how to more effectively use systems like Spark and understand how the design is changing. JVM developers will gain an understanding of how to Python code from data scientist and Python developers while avoiding the traditional trap of needing to rewrite everything.
[DevRelCon Tokyo 2017] Creative Technical Content for Better Developer Experi...Tomomi Imura
?
Let’s say, you are searching certain frameworks, or APIs to satisfy your new project- what if you stumble on some awesome-sounding shiny website, but it comes with very poor documentations. Do you want to try it out, or keep searching something else? Or when you see a GitHub project with no README, how do you feel? I think this developer experience is one of big key factors for you to decide what technologies to use.
User-Experience (UX) focuses on understanding what users' need and value, and provide practical products or services. This human-computer interaction acts the same when the users are developers. The ideas of “Developer Experiences” is to establish a good relationship between developers and platform providers.
So, as a developer evangelist, what can we do to improve DX to get developers' interests?
In this talk, Tomomi Imura will talk about her experiences, and how I create developer-centric contents and docs to drive the community and acquired new developers and customers.
Machine vision and device integration with the Ruby programming language (2008)Jan Wedekind
?
This document provides a summary of a research seminar on machine vision and device integration using the Ruby programming language. The seminar will be held on February 29th, 2008 and will discuss projects using a transmission electron microscope, digital camera, piezo controller and nano indenter as well as a micro camera and piezo drives. It will also discuss proprietary business models versus community development models and differences between GPLv3 and BSD licenses. An introduction to the Ruby programming language is provided including statistics on usage from the Tiobe index and speed comparisons to other languages.
Improving Enterprise Agility via a Lean LensAndrea Darabos
?
The document discusses using lean principles to improve enterprise agility in software development. It defines waste as anything that does not add value from the customer's perspective in either products or processes. Eight common types of waste in software development are identified, including partially completed work and defects. The document proposes a lean lens game to help development teams identify waste in their processes by acting out scenarios and mapping out value streams and waste. Practical takeaways include using value stream mapping and discussing waste regularly in retrospectives to continuously improve.
Puppet Camp Dallas 2014: How Puppet Ops RollsPuppet
?
The document discusses how Puppet Ops manages its Puppet infrastructure. It recommends using good modules from the Forge or GitHub instead of reinventing the wheel. Roles and profiles are used to logically organize Puppet code and consume modules. Hiera drives Puppet configuration with data and allows grouping data by environment. R10k and dynamic environments help keep code agile for multiple developers. Useful tools mentioned include Puppet Query, Puppet Dashboard, Puppet Lint, and Beaker for testing. Hardware recommendations include using a properly sized server to avoid Puppet performance issues.
Evaluating Large Language Models for Your Applications and Why It MattersMia Chang
?
Event: AWS WUG Cloud Talks
Date: 2025-02-11
Description: Confused by the overwhelming metrics for evaluating LLMs? This talk will guide you through key evaluation metrics, tools, and frameworks tailored to specific use cases, including mitigating social biases and extracting interpretable features. Gain clarity on LLM evaluation to build better generative AI applications.
Service: Amazon Bedrock
Speaker: Mia Chang: ML Specialist Solutions Architect at AWS, NLP expert, and author, with extensive experience in AI/ML workloads on the cloud.
Running the first automatic speech recognition (ASR) model with HuggingFace -...Mia Chang
?
Running the first automatic speech recognition (ASR) model with HuggingFace
06-18, 11:00–11:45 (Europe/London), Tower Suite 1
Come and learn your first audio machine learning model with Automatic speech recognition (ASR) use case! ASR has been a popular application like voice-controlled assistants and voice-to-text/speech-to-text applications. These applications take audio clips as input and convert speech signals to text.
This talk is aiming for Python developers or ML practitioners who are knowing Python, and interested in working with audio machine learning use case. I will cover minimum slides about ML algorithm in this talk. Instead, I will walk through types of ASR applications, like automatic subtitling for videos and transcribing meetings. So you will know what are the occasions to work with ASR models. And talk about data processing of audio data, how to do feature extraction, and Fine-tune Wav2Vec2 using HuggingFace. The notebook that presented in the talk is running on Amazon SageMaker, the concept for this talk is cloud agnostic and applies to local computer(on premises) as well.
---
Github: https://github.com/pymia/amazon-sagemaker-fine-tune-and-deploy-wav2vec2-huggingface
Event: PyData London 2022
Date: JUNE 17TH-19TH, 2022
Event link: https://pydata.org/london2022/
Linkedin: http://linkedin.com/in/mia-chang/
7 steps to AI production - global azure bootcamp 2020 KolnMia Chang
?
Session: 7 steps to AI production
Abstract: What was your last AI project? Was it another Kaggle dataset running on Jupyter notebook, hard to reproduce, and don't know how to deploy as an AI service? How to do auto-scaling for the model serving?
How far is the distance from playing with the sample dataset to AI production?
Let's go through 7 steps in the AI application development lifecycle. From data wrangling, reproduce your training, model acceptance to model deployment and management.
Target audience: Data scientist who doesn't know the model serving and Azure DevOps. Backend/DevOps who doesn't know how to help your data team go production.
---
Github: https://github.com/pymia/7-steps-production
Event: Global Azure Bootcamp 2020 Virtual
Date: Apr 25, 2020
Event link: https://www.meetup.com/Azure-Cologne-Meetup/events/266727986/
Linkedin: http://linkedin.com/in/mia-chang/
The content was modified from Google Content Group
Eric ShangKuan(ericsk@google.com)
---
TensorFlow Lite guide( for mobile & IoT )
TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and small binary size.
TensorFlow Lite consists of two main components:
The TensorFlow Lite interpreter:
- optimize models on many different hardware types, like mobile phones, embedded Linux devices, and microcontrollers.
The TensorFlow Lite converter:
- which converts TensorFlow models into an efficient form for use by the interpreter, and can introduce optimizations to improve binary size and performance.
---
Event: PyLadies TensorFlow All-Around
Date: Sep 25, 2019
Event link: https://www.meetup.com/PyLadies-Berlin/events/264205538/
Linkedin: http://linkedin.com/in/mia-chang/
DPS2019 data scientist in the real estate industry Mia Chang
?
This document summarizes a presentation about applying artificial intelligence in the real estate industry. It discusses the different stages of the real estate process and how AI could be used at each stage, including predicting energy usage, processing text in different languages, and automating workflows. It also covers challenges around regulations like GDPR and strategies for developing and deploying AI models, including using transfer learning and version control systems.
Leverage the power of machine learning on windowsMia Chang
?
Note:
The Content was modified from the Microsoft Content team.
Deck Owner: Nitah Onsongo
Tech/Msg Review: Cesar De La Torre, Simon Tao, Clarke Rahrig
---
Event: Insider Dev Tour Berlin
Event Description: Microsoft is going on a world tour with the announcements of Build 2019. The Insider Dev Tour focuses on innovations related to Microsoft 365 from a developer's perspective.
Date: June 7th, 2019
Event link: https://www.microsoft.com/de-de/techwiese/news/best-of-build-insider-dev-tour-am-7-juni-in-berlin.aspx
Linkedin: http://linkedin.com/in/mia-chang/
Develop computer vision applications with azure computer vision apiMia Chang
?
This document discusses developing computer vision applications using the Azure Computer Vision API. It provides an overview of computer vision and AI development on Azure. It also discusses using emotion recognition in chatbots and provides references to computer vision papers, datasets, and tools like the Azure Machine Learning Workbench. The document includes examples of computer vision tasks like object detection and segmentation and provides a small demo of emotion detection.
This document summarizes chapters 5 and 6 from a book on unit testing. Chapter 5 discusses why isolation frameworks are useful for creating fake objects more easily than hand-coding mocks. It also covers simulating fake values and testing events. Chapter 6 distinguishes between constrained and unconstrained isolation frameworks and discusses features that support future-proofing and usability of frameworks. Both chapters emphasize that isolation frameworks make testing easier, faster and less error-prone compared to manually writing mocks.
CloudMonitor - Architecture Audit Review February 2025.pdfRodney Joyce
?
CloudMonitor FinOps is now a Microsoft Certified solution in the Azure Marketplace. This little badge means that we passed a 3rd-party Technical Audit as well as met various sales KPIs and milestones over the last 12 months.
We used our existing Architecture docs for CISOs and Cloud Architects to craft an Audit Response - I've shared it below to help others obtain their cert.
Interestingly, 90% of our customers are in the USA, with very few in Australia. This is odd as the first thing I hear in every meetup and conference, from partners, customers and Microsoft, is that they want to optimise their cloud spend! But very few Australian companies are using the FinOps Framework to lower Azure costs.
Valkey 101 - SCaLE 22x March 2025 Stokes.pdfDave Stokes
?
An Introduction to Valkey, Presented March 2025 at the Southern California Linux Expo, Pasadena CA. Valkey is a replacement for Redis and is a very fast in memory database, used to caches and other low latency applications. Valkey is open-source software and very fast.
Optimizing Common Table Expressions in Apache Hive with CalciteStamatis Zampetakis
?
In many real-world queries, certain expressions may appear multiple times, requiring repeated computations to construct the final result. These recurring computations, known as common table expressions (CTEs), can be explicitly defined in SQL queries using the WITH clause or implicitly derived through transformation rules. Identifying and leveraging CTEs is essential for reducing the cost of executing complex queries and is a critical component of modern data management systems.
Apache Hive, a SQL-based data management system, provides powerful mechanisms to detect and exploit CTEs through heuristic and cost-based optimization techniques.
This talk delves into the internals of Hive's planner, focusing on its integration with Apache Calcite for CTE optimization. We will begin with a high-level overview of Hive's planner architecture and its reliance on Calcite in various planning phases. The discussion will then shift to the CTE rewriting phase, highlighting key Calcite concepts and demonstrating how they are employed to optimize CTEs effectively.
Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures
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Play Kaggle with R, Facebook V: Predicting Check Ins
1. 用 R 玩 Kaggle –
臉書打卡點預測
Play kaggle with R, Facebook V: Predicting Check Ins
@Mia (R-Ladies)
library(dplyr)
r-ladies_global %>%
filter(from = 'Taipei', travel_to = 'Lisbon')
2. The Agenda
First Second Third
Hey, Kaggle
# with R-Ladies
# with Masters
Play R
# Warm Up
# EDA, Shiny Apps
# Azure Jupyter
Notebook
Brief Intro
# About
R-Ladies
# About me
2
Last
Q&A
# Recap
# Resource
Sharing
5. Hello!
I am Mia Chang (張懷文).
? Data Scientist, Lecturer
? Member of R-Ladies Taipei
? Co-founder of Azure Taiwan Community
? Microsoft Most Valuable Professionals (MVP) 2017
5
14. “
Warm Up - 關於這個問題背景,問題定義
Three weeks into the eight-week competition,
I climbed to the top of the public leaderboard with
about 50 features
1. the summary data such as the number of historical check ins.
2. historical density of a place candidate, one year prior to the
observation.
3.All features are rescaled if needed in order to result in
similar interpretations for the train and test features.
14
22. # 演算法及結論
#Rcpp
#It was expected that it
would be clearly correlated
with the variation in x and y
but the pattern is not as
obvious. Halfway through the
competition I cracked the
code ...
22
24. Recap
First Second Third
Hey, Kaggle
# with R-Ladies
# with Masters
Play R
# Warm Up
# EDA, Shiny Apps
# Azure Jupyter
Notebook
Brief Intro
# About
R-Ladies
# About me
24
Last
Q&A
# Recap
# Resource
Sharing
25. Action Item
First Second Third
Hi, Kaggle Play R
Get your
partners
Visit R-Ladies
R-Basic too!
25
Then
...
26. Thanks for your listening!
26
Look forward to your visit to R-Ladies Taipei! Also Azure Taiwan!
27. Bye!
I am Mia Chang (張懷文)
? mia5419@gmail.com
? facebook.com/mia5419
27
28. 28
Take Away & Reference
1.Use EDA to help you find
more feature.
2.Go to Kaggle website to get
more resource to help you:
forum, kernels
3.No matter you are
learning R or you are going
to traveling to visit other
R-Ladies, call us for more
resources :)
1. R-Ladies Meetup Page
2. R-Ladies Facebook Group
3. Blog Post by Tom Van de Wiele
- Detail about implementation
4. Github Repository
5. Shiny App by Tom Van de Wiele
- EDA that you can learn more
6. Kaggle Event Page
7. Microsoft Azure Notebooks