Machine Learning Experts - Margaret Mitchell

HuggingFace ยท Advanced ยท๐Ÿ“„ Research Papers Explained ยท4y ago
๐Ÿš€ If you're interested in learning how ML Experts, like Meg, can help accelerate your ML roadmap visit https://bit.ly/3isEbYH Meg is an Ethical AI Researcher at Hugging Face who previously founded & co-led Google's Ethical AI Group. Thanks for watching Machine Learning Experts with Margaret Mitchell ๐ŸŽ‰ In this video you'll hear Meg talk about: - Inclusion & Diversity in Machine Learning - Model Transparency - Women in AI - Machine Learning Bias ๐Ÿ•ค Timestamps 0:00 Intro 1:55 Meg's Background 7:09 When Meg realized the importance of Ethical AI 10:43 Important data ethics applications 12:03 How ML teams can be more aware of harmful bias 13:31 Machine Learning Culture 16:53 Discrimination in Machine Learning 18:26 Inclusion & Diversity 22:47 Diversity in AI 24:23 Model Cards 31:55 Decision thresholds & model transparency 24:30 Meg's Hugging Face Projects 37:22 Meg's impact on AI 40:22 Advice for someone trying to get into ML/AI? 41:26 What industries Meg is most excited to see ML be applied 42:31 Machine Learning bias example 43:39 Will AI take over the world? 45:55 Meg's favorite ML papers 47:42 Lowering the barrier to AI 49:07 Outro ๐Ÿ”— Honorable mentions + links: Emily Bender: https://twitter.com/emilymbender?lang=en Ehud Reiter: https://mobile.twitter.com/ehudreiter Seeing AI: https://www.microsoft.com/en-us/ai/seeing-ai Data Sheets for Datasets: https://arxiv.org/abs/1803.09010 Model Cards: https://modelcards.withgoogle.com/about Model Cards Paper: https://arxiv.org/abs/1810.03993 ๐Ÿ–Š๏ธ Favorite ML Papers: Abeba Birhane's Papers: https://arxiv.org/search/cs?searchtype=author&query=Birhane%2C+A The Values Encoded in ML Research: https://arxiv.org/abs/2106.15590 ๐Ÿค— Find us at: https://bit.ly/3isEbYH ๐Ÿ”ฅ Follow Meg Online: Twitter: https://twitter.com/mmitchell_ai LinkedIn: https://www.linkedin.com/in/margaret-mitchell-9b13429/ Website: http://www.m-mitchell.com
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Playlist

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1 The Future of Natural Language Processing
The Future of Natural Language Processing
HuggingFace
2 Trends in Model Size & Computational Efficiency in NLP
Trends in Model Size & Computational Efficiency in NLP
HuggingFace
3 Increasing Data Usage in Natural Language Processing
Increasing Data Usage in Natural Language Processing
HuggingFace
4 In Domain & Out of Domain Generalization in the Future of NLP
In Domain & Out of Domain Generalization in the Future of NLP
HuggingFace
5 The Limits of NLU & the Rise of NLG in the Future of NLP
The Limits of NLU & the Rise of NLG in the Future of NLP
HuggingFace
6 The Lack of Robustness in the Future of NLP
The Lack of Robustness in the Future of NLP
HuggingFace
7 Inductive Bias, Common Sense, Continual Learning in The Future of NLP
Inductive Bias, Common Sense, Continual Learning in The Future of NLP
HuggingFace
8 Train a Hugging Face Transformers Model with Amazon SageMaker
Train a Hugging Face Transformers Model with Amazon SageMaker
HuggingFace
9 What is Transfer Learning?
What is Transfer Learning?
HuggingFace
10 The pipeline function
The pipeline function
HuggingFace
11 Navigating the Model Hub
Navigating the Model Hub
HuggingFace
12 Transformer models: Decoders
Transformer models: Decoders
HuggingFace
13 The Transformer architecture
The Transformer architecture
HuggingFace
14 Transformer models: Encoder-Decoders
Transformer models: Encoder-Decoders
HuggingFace
15 Transformer models: Encoders
Transformer models: Encoders
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16 Keras introduction
Keras introduction
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17 The push to hub API
The push to hub API
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18 Fine-tuning with TensorFlow
Fine-tuning with TensorFlow
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19 Learning rate scheduling with TensorFlow
Learning rate scheduling with TensorFlow
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20 TensorFlow Predictions and metrics
TensorFlow Predictions and metrics
HuggingFace
21 Welcome to the Hugging Face course
Welcome to the Hugging Face course
HuggingFace
22 The tokenization pipeline
The tokenization pipeline
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23 Supercharge your PyTorch training loop with Accelerate
Supercharge your PyTorch training loop with Accelerate
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24 The Trainer API
The Trainer API
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25 Batching inputs together (PyTorch)
Batching inputs together (PyTorch)
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26 Batching inputs together (TensorFlow)
Batching inputs together (TensorFlow)
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27 Hugging Face Datasets overview (Pytorch)
Hugging Face Datasets overview (Pytorch)
HuggingFace
28 Hugging Face Datasets overview (Tensorflow)
Hugging Face Datasets overview (Tensorflow)
HuggingFace
29 What is dynamic padding?
What is dynamic padding?
HuggingFace
30 What happens inside the pipeline function? (PyTorch)
What happens inside the pipeline function? (PyTorch)
HuggingFace
31 What happens inside the pipeline function? (TensorFlow)
What happens inside the pipeline function? (TensorFlow)
HuggingFace
32 Instantiate a Transformers model (PyTorch)
Instantiate a Transformers model (PyTorch)
HuggingFace
33 Instantiate a Transformers model (TensorFlow)
Instantiate a Transformers model (TensorFlow)
HuggingFace
34 Preprocessing sentence pairs (PyTorch)
Preprocessing sentence pairs (PyTorch)
HuggingFace
35 Preprocessing sentence pairs (TensorFlow)
Preprocessing sentence pairs (TensorFlow)
HuggingFace
36 Write your training loop in PyTorch
Write your training loop in PyTorch
HuggingFace
37 Managing a repo on the Model Hub
Managing a repo on the Model Hub
HuggingFace
38 Chapter 1 Live Session with Sylvain
Chapter 1 Live Session with Sylvain
HuggingFace
39 Chapter 2 Live Session with Lewis
Chapter 2 Live Session with Lewis
HuggingFace
40 The push to hub API
The push to hub API
HuggingFace
41 Chapter 2 Live Session with Sylvain
Chapter 2 Live Session with Sylvain
HuggingFace
42 Chapter 3 live sessions with Lewis (PyTorch)
Chapter 3 live sessions with Lewis (PyTorch)
HuggingFace
43 Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
HuggingFace
44 Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
HuggingFace
45 Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
HuggingFace
46 Chapter 4 live sessions with Omar
Chapter 4 live sessions with Omar
HuggingFace
47 Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
HuggingFace
48 Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
HuggingFace
49 Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
HuggingFace
50 [Webinar] How to add machine learning capabilities with just a few lines of code
[Webinar] How to add machine learning capabilities with just a few lines of code
HuggingFace
51 Hugging Face + Zapier Demo Video
Hugging Face + Zapier Demo Video
HuggingFace
52 Hugging Face + Google Sheets Demo
Hugging Face + Google Sheets Demo
HuggingFace
53 Hugging Face Infinity Launch - 09/28
Hugging Face Infinity Launch - 09/28
HuggingFace
54 Build and Deploy a Machine Learning App in 2 Minutes
Build and Deploy a Machine Learning App in 2 Minutes
HuggingFace
55 Hugging Face Infinity - GPU Walkthrough
Hugging Face Infinity - GPU Walkthrough
HuggingFace
56 Otto - ๐Ÿค— Infinity Case Study
Otto - ๐Ÿค— Infinity Case Study
HuggingFace
57 Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it
Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it
HuggingFace
58 Workshop: Going Production: Deploying, Scaling & Monitoring Hugging Face Transformer models
Workshop: Going Production: Deploying, Scaling & Monitoring Hugging Face Transformer models
HuggingFace
59 ๐Ÿค— Tasks: Causal Language Modeling
๐Ÿค— Tasks: Causal Language Modeling
HuggingFace
60 ๐Ÿค— Tasks: Masked Language Modeling
๐Ÿค— Tasks: Masked Language Modeling
HuggingFace

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Chapters (20)

Intro
1:55 Meg's Background
7:09 When Meg realized the importance of Ethical AI
10:43 Important data ethics applications
12:03 How ML teams can be more aware of harmful bias
13:31 Machine Learning Culture
16:53 Discrimination in Machine Learning
18:26 Inclusion & Diversity
22:47 Diversity in AI
24:23 Model Cards
31:55 Decision thresholds & model transparency
24:30 Meg's Hugging Face Projects
37:22 Meg's impact on AI
40:22 Advice for someone trying to get into ML/AI?
41:26 What industries Meg is most excited to see ML be applied
42:31 Machine Learning bias example
43:39 Will AI take over the world?
45:55 Meg's favorite ML papers
47:42 Lowering the barrier to AI
49:07 Outro
Up next
Microsoft Research Forum | Season 2, Episode 4
Microsoft Research
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