LLM Accuracy, Performance, and Trade-Offs

AI, large language models    |    Intermediate
  • 18 videos | 1h 52m 48s
  • Includes Assessment
  • Earns a Badge
Large language models (LLMs) are game changers in AI, but achieving their impressive accuracy and performance benefits involves navigating some fascinating trade-offs. In this course, learn how to balance performance, model size, and computational resources and how larger models typically offer better performance on complex tasks. Next, explore how evaluating the trade-offs between model size, accuracy, and resource consumption is crucial, particularly when selecting between in-house and public LLMs. Finally, discover how key metrics like perplexity and F1 score play a role in determining the coherence and quality of the text these models generate. After completing this course, you will be able to apply best practices to choose the right LLM for your needs.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline precision in large language models (llms) and how to improve precision
    Describe what recall is in llms and best practices for optimizing recall
    Identify the key concepts of f1 scores in llms and the key factors that influence f1 scores
    Examine llm performance on text classification tasks using precision
    Analyze llm performance on text classification tasks using recall
    Evaluate llm performance on text classification tasks using f1 score
    Describe the trade-offs between accuracy when selecting an llm
    Recognize the trade-offs between model size when selecting an llm
  • Identify the trade-offs between computational cost when selecting an llm
    Outline how perplexity and f1 score impact the coherence and quality of generated text outputs
    Describe how to apply best practices for choosing between in-house and public llms based on accuracy needs and resource availability
    Identify how to optimize llm performance through fine-tuning, balancing accuracy and resource consumption
    Fine-tune a pre-trained llm for improved accuracy on a domain-specific task
    Compare metrics of a fine-tuned model to a base model
    Recognize how different models perform across a variety of tasks, identifying strengths and weaknesses based on performance metrics
    Describe how to assess the quality of llms by analyzing their ability to perform across different tasks, considering task-specific performance and overall output quality
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 13s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 6m 53s
    After completing this video, you will be able to outline precision in large language models (LLMs) and how to improve precision. FREE ACCESS
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    3.  Large Language Model Recall
    7m 40s
    Upon completion of this video, you will be able to describe what recall is in LLMs and best practices for optimizing recall. FREE ACCESS
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    4.  Large Language Model F1 Scores
    6m 8s
    In this video, we will identify the key concepts of F1 scores in LLMs and the key factors that influence F1 scores. FREE ACCESS
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    5.  Examining Large Language Model Precision
    8m 40s
    Learn how to examine LLM performance on text classification tasks using precision. FREE ACCESS
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    6.  Analyzing Large Language Model Recall
    6m 41s
    During this video, discover how to analyze LLM performance on text classification tasks using recall. FREE ACCESS
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    7.  Evaluating Large Language Model F1 Scores
    5m 38s
    In this video, find out how to evaluate LLM performance on text classification tasks using F1 score. FREE ACCESS
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    8.  Large Language Model Accuracy
    7m 47s
    After completing this video, you will be able to describe the trade-offs between accuracy when selecting an LLM. FREE ACCESS
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    9.  Large Language Model Size
    7m 23s
    Through this video, you will be able to recognize the trade-offs between model size when selecting an LLM. FREE ACCESS
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    10.  Large Language Model Computational Cost
    6m 25s
    In this video, we will identify the trade-offs between computational cost when selecting an LLM. FREE ACCESS
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    11.  Large Language Model Perplexity
    6m 3s
    Upon completion of this video, you will be able to outline how perplexity and F1 score impact the coherence and quality of generated text outputs. FREE ACCESS
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    12.  Large Language Model Selection Best Practices
    7m 21s
    After completing this video, you will be able to describe how to apply best practices for choosing between in-house and public LLMs based on accuracy needs and resource availability. FREE ACCESS
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    13.  Large Language Model Fine-Tuning
    6m 36s
    Through this video, you will be able to identify how to optimize LLM performance through fine-tuning, balancing accuracy and resource consumption. FREE ACCESS
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    14.  Fine-Tuning Large Language Models
    7m 3s
    Discover how to fine-tune a pre-trained LLM for improved accuracy on a domain-specific task. FREE ACCESS
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    15.  Comparing Metrics of Fine-Tuned Large Language Models
    6m 23s
    In this video, we will compare metrics of a fine-tuned model to a base model. FREE ACCESS
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    16.  Large Language Model Performance Metrics
    6m 18s
    Through this video, you will be able to recognize how different models perform across a variety of tasks, identifying strengths and weaknesses based on performance metrics. FREE ACCESS
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    17.  Large Language Model Overall Output Quality
    6m 6s
    Upon completion of this video, you will be able to describe how to assess the quality of LLMs by analyzing their ability to perform across different tasks, considering task-specific performance and overall output quality. FREE ACCESS
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    18.  Course Summary
    1m 32s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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