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Recorded on February 2, 2022.
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RapVS.AI - Human Rappers Battle AI Computer Rapper (Powered by Microsoft Semantic Kernel)
Web: https://rapvs.ai
Connect with us:
https://instagram.com/rapvsai
https://twitter.com/rapvsai
RapVS.AI features a rap battle against an AI computer rapper, blending culture, language, and AI in a modern-day remix reminiscent of the 1997 classic, IBM Deep Blue versus Chess Grandmaster, a human vs machine competition. Created by @Rebrand Cities (Prompt Camp) and powered by Microsoft's #SemanticKernel technology.
Featured Rappers:
Tray Little - https://instagram.com/traylittle
BFree - https://instagram.com/bfree
Monalyse - https://instagram.com/themonalyse
Coming Soon:
RapVS.AI @ Black Tech Week in Cincinnati, OH July 18 - 20, 2023
About
[Prompt Camp]
Prompt Camp is a comprehensive workshop and summit series that aims to make AI, ChatGPT, and generative AI accessible to all people. We are the portal for the people. Our virtual and in-person training series are designed to develop the next generation of creative directors in SMBs, creative agencies, and entrepreneurs.
[Rebrand Cities]
Rebrand Cities is architecting digital transformations for brands of the future. Using technology to reimagine luxury, fashion, culture, cities, small businesses, and wellness with new modes of creativity and thinking, making them more human in the digital economy. Rebrand Cities has an audacious goal to digitally transform 10,000 businesses in cities globally. To date, Rebrand Cities has worked with 1,000+ SMBs in ten cities and three countries (including Haiti).
[Video Credit: Cy Abdelnor - Angles Media - https://instagram.com/cylent__
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Tools to Improve Training Data - Talking Language AI Ep#2
Vincent Warmerdam builds a lot of NLP tools (https://github.com/koaning). Many of these tools target the scikit-learn ecosystem and there's a theme of labeling across many of them. A recent focus of his stack of tools is to improve training data. In this video, Vincent and Jay discuss a few of these tools and show how they work together.
These tools are discussed in the video:
- Human-learn: a toolkit to build human-based scikit-learn components
- Doubtlab: a toolkit to help find doubtful labels in data
- Embetter: A library that makes it very easy to use embeddings in scikit-learn
- Bulk: a library that uses embeddings to leverage bulk labeling
The talk includes live demos for each and to show how some simple tricks can go a long way.
===
Join the Cohere Discord: https://discord.gg/co-mmunity
Discussion thread for this episode (feel free to ask questions):
https://discord.com/channels/954421988141711382/1042163984817721527
Vincent on Twitter: https://twitter.com/fishnets88
human-learn: Natural Intelligence is still a pretty good idea.
https://koaning.github.io/human-learn/index.html
https://github.com/koaning/human-learn/
doubtlab: Doubt your data, find bad labels.
https://koaning.github.io/doubtlab/
https://github.com/koaning/doubtlab
embetter: just a bunch of useful embeddings
https://github.com/koaning/embetter
bulk: A Simple Bulk Labelling Tool
https://github.com/koaning/bulk
Calmcode: Code. Simply. Clearly. Calmly.
Video tutorials for modern ideas and open source tools.
https://calmcode.io/
About The Speaker:
Vincent worked as an engineer, consultant, researcher, team lead, and educator in the past. Currently, he works as a Machine Learning Engineer over at Explosion, the company behind spaCy and Prodi.gy. In addition to his work at Explosion, he also maintains many scikit-learn-related plugins as well as a popular learning resource over at calmcode.io. He's also a frequent speaker at conferences where he defends common sense over the hype in ML.
===
Contents
0:00 Introduction
3:06 Tools for Data Quality
9:18 human-learn: Natural Intelligence is still a pretty good idea.
12:28 human-learn: demo
27:11 doubtlab: Doubt your data, find bad labels.
42:35 embetter: just a bunch of useful embeddings
46:16 embetter: demo
58:10 bulk: A Simple Bulk Labelling Tool
1:00:20 bulk demo: exploring text data
1:10:47 bulk demo: exploring images
1:16:20 Why use the scikit learn API? What are the benefits and limitations?
1:17:22 Programmer productivity tips
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