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Pretrain Vision and Large Language Models in Python

You're reading from   Pretrain Vision and Large Language Models in Python End-to-end techniques for building and deploying foundation models on AWS

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804618257
Length 258 pages
Edition 1st Edition
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Author (1):
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Emily Webber Emily Webber
Author Profile Icon Emily Webber
Emily Webber
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Before Pretraining
2. Chapter 1: An Introduction to Pretraining Foundation Models FREE CHAPTER 3. Chapter 2: Dataset Preparation: Part One 4. Chapter 3: Model Preparation 5. Part 2: Configure Your Environment
6. Chapter 4: Containers and Accelerators on the Cloud 7. Chapter 5: Distribution Fundamentals 8. Chapter 6: Dataset Preparation: Part Two, the Data Loader 9. Part 3: Train Your Model
10. Chapter 7: Finding the Right Hyperparameters 11. Chapter 8: Large-Scale Training on SageMaker 12. Chapter 9: Advanced Training Concepts 13. Part 4: Evaluate Your Model
14. Chapter 10: Fine-Tuning and Evaluating 15. Chapter 11: Detecting, Mitigating, and Monitoring Bias 16. Chapter 12: How to Deploy Your Model 17. Part 5: Deploy Your Model
18. Chapter 13: Prompt Engineering 19. Chapter 14: MLOps for Vision and Language 20. Chapter 15: Future Trends in Pretraining Foundation Models 21. Index 22. Other Books You May Enjoy

Planning future experiments

Now that you have an idea of what model size you’d like to target given your compute budget and data constraints, let’s learn how to think about each run of your job as an experiment. Fundamentally, each stage in the machine learning process is ultimately a unique experiment. Some of your inputs to each stage in the project stay the same; you could call these your dependent variables. Some of your inputs to the project change; these are your independent variables. it takes time to build up skills on your project Simply put, change something and see what happens. Just make sure you’re only changing one thing, so it’s empirically clear what the result is!

It’s critical to understand that the whole scope of your project is not going to happen all at once. A lot of this is because it takes time to build up skills on your time. Even if you are starting with a completely experienced team, which frankly happens very rarely...

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