OData只定規格,不限制實作,當然,它由微軟提出,ASP.NET Web API v1 就支援 OData,在 ASP.NET Web API v2 一路支援至 OData v3(與有限的v4)。我們談 OData 規格也談 ASP.NET Web API 實作,如何利用 OData 來擴充你的 ASP.NET Web API,讓你開發出來的 RESTFul API 能應付多變的需求,以提升加速開發(少寫一行扣,就少一隻蟲!)。
this is 3 hours speech for non IT related students.
I briefly introduce chat bot application, learning path, restful api, and Microsoft bot framework. Finally I run my skype bot project and explain how it works.
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/
More Related Content
Similar to twMVC#29 -Learning Machine Learning with Movie Recommendation (20)
OData只定規格,不限制實作,當然,它由微軟提出,ASP.NET Web API v1 就支援 OData,在 ASP.NET Web API v2 一路支援至 OData v3(與有限的v4)。我們談 OData 規格也談 ASP.NET Web API 實作,如何利用 OData 來擴充你的 ASP.NET Web API,讓你開發出來的 RESTFul API 能應付多變的需求,以提升加速開發(少寫一行扣,就少一隻蟲!)。
this is 3 hours speech for non IT related students.
I briefly introduce chat bot application, learning path, restful api, and Microsoft bot framework. Finally I run my skype bot project and explain how it works.
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)
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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
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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.
Play Kaggle with R, Facebook V: Predicting Check InsMia Chang
?
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.
2. http://mvc.tw
About me
2
? Algorithm Research
? Machine Learning / Deep LearningData Scientist
Community
? twMVC
? Core Staff R-Ladies
? Co-Founder Azure Taiwan Community
? Co-Founder Tech Podcast Night
Contribute ? 2017 Data Platform MVP
? MVP Award Blog Technical Committee
? Workshop / Hackathon Mentor
20. http://mvc.tw
■ Analysis from MovieTweetings
225,000 ratings for
15,742 movies
by 26,770 users
資料集
20
ref: Paper MovieTweetings , pic: edit from here
29. http://mvc.tw
Checkpoint 03 - Training Model
29
選擇想要Score的方式
Predict ratings for a given user and item
Recommend items to a given user
Find users related to a given user
Find items related to a given item
依據不同方式放進input
輸出的結果(Visualize)
36. http://mvc.tw
■ 實驗
AzureML Recommender: Movie recommendation
■ 演算法論文
MS Research Matchbox: Large Scale Bayesian Recommendations
■ 推薦系統背景知識
MS Technet Recommendations Everywhere
Read More
36