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Generative AI with Large Language Models: Hands-On Training feat. Hugging Face and PyTorch Lightning
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TOPIC SUMMARY
Module 1: Introduction to Large Language Models (LLMs)
- A Brief History of Natural Language Processing (NLP)
- Transformers
- Subword Tokenization
- Autoregressive vs Autoencoding Models
- ELMo, BERT and T5
- The GPT (Generative Pre-trained Transformer) Family
- LLM Application Areas
Module 2: The Breadth of LLM Capabilities
- LLM Playgrounds
- Staggering GPT-Family progress
- Key Updates with GPT-4
- Calling OpenAI APIs, including GPT-4
Module 3: Training and Deploying LLMs
- Hardware Options (e.g., CPU, GPU, TPU, IPU, AWS chips)
- The Hugging Face Transformers Library
- Best Practices for Efficient LLM Training
- Parameter-efficient fine-tuning (PEFT) with low-rank adaptation (LoRA)
- Open-Source Pre-Trained LLMs
- LLM Training with PyTorch Lightning
- Multi-GPU Training
- LLM Deployment Considerations
- Monitoring LLMs in Production
Module 4: Getting Commercial Value from LLMs
- Supporting ML with LLMs
- Tasks that can be Automated
- Tasks that can be Augmented
- Best Practices for Successful A.I. Teams and Projects
- What's Next for A.I.
CHAPTERS
0:00 Intro
5:56 Module 1: Introduction to Large Language Models (LLMs)
18:17 The Models that Shaped the Field
34:40 The GPT Family: A Closer Look
38:09 Module 2: The Breadth of LLM Capabilities
49:12 GPT-4 and OpenAI APIs
57:48 Module 3: Training and Deploying LLMs
1:09:52 Advanced Training Techniques and Open-Source Options
1:45:56 Training with PyTorch Lightning and Multi-GPU Training
2:09:33 Deployment and Monitoring of LLMs
2:10:30 Module 4: Getting Commercial Value from LLMs
ABSTRACT
At an unprecedented pace, Large Language Models like GPT-4 are transforming the world in general and the field of data science in particular. This two-hour training introduces deep learning transformer architectures including LLMs. Critically, it also demonstrates the breadth of capabilities of state-of-the-art LLMs like GPT-4 can deliver, including for dramatically revolutionizing the development of machine learning models and commercially successful data-driven products, accelerating the creative capacities of data scientists and pushing them in the direction of being data product managers. Brought to life via hands-on code demos that leverage the Hugging Face and PyTorch Lightning Python libraries, this training covers the full lifecycle of LLM development, from training to production deployment.
ABOUT THE PRESENTER
Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.
STAY IN TOUCH
Module 1: Introduction to Large Language Models (LLMs)
- A Brief History of Natural Language Processing (NLP)
- Transformers
- Subword Tokenization
- Autoregressive vs Autoencoding Models
- ELMo, BERT and T5
- The GPT (Generative Pre-trained Transformer) Family
- LLM Application Areas
Module 2: The Breadth of LLM Capabilities
- LLM Playgrounds
- Staggering GPT-Family progress
- Key Updates with GPT-4
- Calling OpenAI APIs, including GPT-4
Module 3: Training and Deploying LLMs
- Hardware Options (e.g., CPU, GPU, TPU, IPU, AWS chips)
- The Hugging Face Transformers Library
- Best Practices for Efficient LLM Training
- Parameter-efficient fine-tuning (PEFT) with low-rank adaptation (LoRA)
- Open-Source Pre-Trained LLMs
- LLM Training with PyTorch Lightning
- Multi-GPU Training
- LLM Deployment Considerations
- Monitoring LLMs in Production
Module 4: Getting Commercial Value from LLMs
- Supporting ML with LLMs
- Tasks that can be Automated
- Tasks that can be Augmented
- Best Practices for Successful A.I. Teams and Projects
- What's Next for A.I.
CHAPTERS
0:00 Intro
5:56 Module 1: Introduction to Large Language Models (LLMs)
18:17 The Models that Shaped the Field
34:40 The GPT Family: A Closer Look
38:09 Module 2: The Breadth of LLM Capabilities
49:12 GPT-4 and OpenAI APIs
57:48 Module 3: Training and Deploying LLMs
1:09:52 Advanced Training Techniques and Open-Source Options
1:45:56 Training with PyTorch Lightning and Multi-GPU Training
2:09:33 Deployment and Monitoring of LLMs
2:10:30 Module 4: Getting Commercial Value from LLMs
ABSTRACT
At an unprecedented pace, Large Language Models like GPT-4 are transforming the world in general and the field of data science in particular. This two-hour training introduces deep learning transformer architectures including LLMs. Critically, it also demonstrates the breadth of capabilities of state-of-the-art LLMs like GPT-4 can deliver, including for dramatically revolutionizing the development of machine learning models and commercially successful data-driven products, accelerating the creative capacities of data scientists and pushing them in the direction of being data product managers. Brought to life via hands-on code demos that leverage the Hugging Face and PyTorch Lightning Python libraries, this training covers the full lifecycle of LLM development, from training to production deployment.
ABOUT THE PRESENTER
Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.
STAY IN TOUCH
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