Machine Learning Experts - Lewis Tunstall

HuggingFace ยท Beginner ยท๐Ÿง  Large Language Models ยท3y ago
๐Ÿš€ If you're interested in learning how ML Experts, like Lewis, can help accelerate your ML roadmap visit: https://bit.ly/3DDt3lD to learn more. Lewis is a Machine Learning Engineer at Hugging Face where he works on applying Transformers to automate business processes and solve MLOps challenges. Lewis has built ML applications for startups and enterprises in the domains of NLP, topological data analysis, and time series. In this video you'll hear Lewis talk about: - His work on Transformers - Deploying models into the real world - ONNX serialization - Large scale model evaluation - Benchmarโ€ฆ
Watch on YouTube โ†— (saves to browser)

Chapters (20)

Intro
2:38 Lewis Tunstallโ€™s Background
3:37 Natural Language Processing with Transformers
6:21 Deploying models into production
7:24 Transformers & ONNX format
8:29 OpenAI GPT-2 / Auto-generated text
10:51 Hugging Face Course
12:37 Machine Learning Applications
16:33 Large Scale Model Evaluation
17:49 Machine Learning Benchmarks
20:14 Common Machine Learning Mistakes
22:54 What would you do differently at the start of your career?
25:21 Best advice for someone looking to get into AI/ML?
26:22 Will AI take over the world?
27:42 When will robots be in homes everywhere?
29:18 DeepMind Podcast
31:08 Favorite Machine Learning Papers
33:56 What is the meaning of life?
35:47 Checkout Lewisโ€™s Natural Language Processing with Transformers book
39:55 Where you can follow Lewis online

Playlist

Uploads from HuggingFace ยท HuggingFace ยท 0 of 60

โ† Previous Next โ†’
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 and use a NLP model in 10 mins!
Train and use a NLP model in 10 mins!
HuggingFace
 9 Automatic text classification in a few lines of code
Automatic text classification in a few lines of code
HuggingFace
 10 Train a Hugging Face Transformers Model with Amazon SageMaker
Train a Hugging Face Transformers Model with Amazon SageMaker
HuggingFace
 11 What is Transfer Learning?
What is Transfer Learning?
HuggingFace
 12 The pipeline function
The pipeline function
HuggingFace
 13 Navigating the Model Hub
Navigating the Model Hub
HuggingFace
 14 Character-based tokenizers
Character-based tokenizers
HuggingFace
 15 Transformer models: Decoders
Transformer models: Decoders
HuggingFace
 16 The Transformer architecture
The Transformer architecture
HuggingFace
 17 Transformer models: Encoder-Decoders
Transformer models: Encoder-Decoders
HuggingFace
 18 Transformer models: Encoders
Transformer models: Encoders
HuggingFace
 19 Keras introduction
Keras introduction
HuggingFace
 20 The push to hub API
The push to hub API
HuggingFace
 21 Subword-based tokenizers
Subword-based tokenizers
HuggingFace
 22 Fine-tuning with TensorFlow
Fine-tuning with TensorFlow
HuggingFace
 23 Learning rate scheduling with TensorFlow
Learning rate scheduling with TensorFlow
HuggingFace
 24 TensorFlow Predictions and metrics
TensorFlow Predictions and metrics
HuggingFace
 25 Tokenizers Overview
Tokenizers Overview
HuggingFace
 26 Word-based tokenizers
Word-based tokenizers
HuggingFace
 27 Welcome to the Hugging Face course
Welcome to the Hugging Face course
HuggingFace
 28 The tokenization pipeline
The tokenization pipeline
HuggingFace
 29 Supercharge your PyTorch training loop with Accelerate
Supercharge your PyTorch training loop with Accelerate
HuggingFace
 30 The Trainer API
The Trainer API
HuggingFace
 31 Batching inputs together (PyTorch)
Batching inputs together (PyTorch)
HuggingFace
 32 Batching inputs together (TensorFlow)
Batching inputs together (TensorFlow)
HuggingFace
 33 Hugging Face Datasets overview (Pytorch)
Hugging Face Datasets overview (Pytorch)
HuggingFace
 34 Hugging Face Datasets overview (Tensorflow)
Hugging Face Datasets overview (Tensorflow)
HuggingFace
 35 What is dynamic padding?
What is dynamic padding?
HuggingFace
 36 What happens inside the pipeline function? (PyTorch)
What happens inside the pipeline function? (PyTorch)
HuggingFace
 37 What happens inside the pipeline function? (TensorFlow)
What happens inside the pipeline function? (TensorFlow)
HuggingFace
 38 Instantiate a Transformers model (PyTorch)
Instantiate a Transformers model (PyTorch)
HuggingFace
 39 Instantiate a Transformers model (TensorFlow)
Instantiate a Transformers model (TensorFlow)
HuggingFace
 40 Preprocessing sentence pairs (PyTorch)
Preprocessing sentence pairs (PyTorch)
HuggingFace
 41 Preprocessing sentence pairs (TensorFlow)
Preprocessing sentence pairs (TensorFlow)
HuggingFace
 42 Write your training loop in PyTorch
Write your training loop in PyTorch
HuggingFace
 43 Managing a repo on the Model Hub
Managing a repo on the Model Hub
HuggingFace
 44 Chapter 1 Live Session with Sylvain
Chapter 1 Live Session with Sylvain
HuggingFace
 45 Chapter 2 Live Session with Lewis
Chapter 2 Live Session with Lewis
HuggingFace
 46 The push to hub API
The push to hub API
HuggingFace
 47 Chapter 2 Live Session with Sylvain
Chapter 2 Live Session with Sylvain
HuggingFace
 48 Chapter 3 live sessions with Lewis (PyTorch)
Chapter 3 live sessions with Lewis (PyTorch)
HuggingFace
 49 Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
HuggingFace
 50 Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
HuggingFace
 51 Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
HuggingFace
 52 Chapter 4 live sessions with Omar
Chapter 4 live sessions with Omar
HuggingFace
 53 Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
HuggingFace
 54 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
 55 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
 56 [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
 57 Hugging Face + Zapier Demo Video
Hugging Face + Zapier Demo Video
HuggingFace
 58 Hugging Face + Google Sheets Demo
Hugging Face + Google Sheets Demo
HuggingFace
 59 Hugging Face Infinity Launch - 09/28
Hugging Face Infinity Launch - 09/28
HuggingFace
 60 Introducing AutoNLP (Trailer)
Introducing AutoNLP (Trailer)
HuggingFace
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Next Up
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)