ML Experts - Sasha Luccioni
๐ If you're interested in learning how ML Experts, like Sasha, can help accelerate your ML roadmap visit: https://bit.ly/3FcyWXI to learn more.
Sasha is a Research Scientist at Hugging Face where she works on creating ethical data and model development practices, developing tools and frameworks for data-centric AI practice, and contributing to democratizing AI and Machine Learning for all.
In this video you'll hear Sasha talk about:
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๐ค Timestamps
0:00 Intro
1:38 Sasha Luccioniโs background
3:22 What Sashaโs excited to work on
5:15 Carbon footprint of an email (among other things)
6:23 โฆ
Watch on YouTube โ
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Chapters (33)
Intro
1:38
Sasha Luccioniโs background
3:22
What Sashaโs excited to work on
5:15
Carbon footprint of an email (among other things)
6:23
Environmental impact of tech/AI
7:00
Measuring emissions
7:56
Computing region (cloud instances) emission
8:28
Energy grids
9:48
Nudge theory
10:55
How ML teams & engineers can become more aware of their environmental impact
12:22
Tackling climate change with machine learning
12:50
Renewable energy + time series prediction
14:25
Detecting deforestation & wildfires
15:43
Cost + benefit of environmental efforts (climatechange.ai)
17:40
Common mistakes do you see ML Engineers/Teams make?
19:06
How ML models fail to get in front of the right people
22:17
The importance of meaning
25:32
The importance of ML accessibility & democratization
27:46
Attaining data (jungle camera example)
31:36
Tips for ML teams/engineers lacking necessary data
32:15
Soup kitchen dataset example
34:40
Community involvement
35:26
What industries are you most excited to see ML be applied?
37:13
If you could go back and do one thing differently at the start of your ML career
38:44
Mathematics - how much do you need to know?
40:51
Best advice for someone looking to get into AI/ML?
42:37
Classifying butterflies
45:06
Will AI take over the world?
47:22
Gardening
49:00
The value of tangible projects
49:24
Favorite Machine learning papers?
51:35
Model evaluation
53:36
Where you can follow Sasha online
Playlist
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Increasing Data Usage in Natural Language Processing
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In Domain & Out of Domain Generalization in the Future of NLP
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The pipeline function
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Character-based tokenizers
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Transformer models: Decoders
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The Transformer architecture
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Transformer models: Encoder-Decoders
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Transformer models: Encoders
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Keras introduction
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The push to hub API
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Subword-based tokenizers
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Fine-tuning with TensorFlow
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Learning rate scheduling with TensorFlow
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TensorFlow Predictions and metrics
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Tokenizers Overview
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Word-based tokenizers
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Welcome to the Hugging Face course
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The tokenization pipeline
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The Trainer API
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Batching inputs together (PyTorch)
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Hugging Face Datasets overview (Pytorch)
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What is dynamic padding?
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What happens inside the pipeline function? (PyTorch)
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What happens inside the pipeline function? (TensorFlow)
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Instantiate a Transformers model (PyTorch)
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Instantiate a Transformers model (TensorFlow)
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Preprocessing sentence pairs (PyTorch)
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Preprocessing sentence pairs (TensorFlow)
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Write your training loop in PyTorch
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Managing a repo on the Model Hub
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Chapter 1 Live Session with Sylvain
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Chapter 2 Live Session with Lewis
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The push to hub API
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Chapter 2 Live Session with Sylvain
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Chapter 3 live sessions with Lewis (PyTorch)
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Day 1 Talks: JAX, Flax & Transformers ๐ค
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Day 2 Talks: JAX, Flax & Transformers ๐ค
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Day 3 Talks JAX, Flax, Transformers ๐ค
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Chapter 4 live sessions with Omar
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Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
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Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
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Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
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[Webinar] How to add machine learning capabilities with just a few lines of code
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Hugging Face + Zapier Demo Video
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