An Introduction to GPT Models

Artificial Intelligence, GPT    |    Beginner
  • 12 videos | 1h 53m 17s
  • Includes Assessment
  • Earns a Badge
Generative Pre-trained Transformer (GPT) models are advanced artificial intelligence (AI) systems designed to understand and generate human-like text based on the information they've been trained on. These models can perform a wide range of language tasks, from writing stories to answering questions, by learning patterns in vast amounts of text data. In this course, you will dive into the world of GPT models and the foundational models that are pivotal to the development of the GPT-n series. You will gain an understanding of the terminology and concepts that make GPT models outstanding in performing natural language processing tasks. Next, you will explore the concept of attention in language models and explore the mechanics of the Transformer architecture, the cornerstone of GPT models. Finally, you will explore the details of the GPT model. You will discover methods used to adapt these models for particular tasks through supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and techniques such as prompt engineering and prompt tuning.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Summarize what generative pre-trained transformer (gpt) models are and how they are used
    Outline the concept and use cases of foundation models
    Provide an overview of the concept of attention in models
    Outline the structure of inputs to the transformer
    Outline the working of transformer attention layers
  • Outline how gpt models work
    Recognize the importance of foundation models
    Provide an overview of supervised fine-tuning (sft) for model alignment
    Outline how reinforcement learning from human feedback (rlhf) helps improve model performance
    Contrast prompt engineering and prompt tuning
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 58s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 10m 25s
    After completing this video, you will be able to summarize what Generative Pre-trained Transformer (GPT) models are and how they are used. FREE ACCESS
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    3.  Foundation Models and the GPT-n Series
    12m 55s
    Upon completion of this video, you will be able to outline the concept and use cases of foundation models. FREE ACCESS
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    4.  Attention Is All You Need
    11m 32s
    After completing this video, you will be able to provide an overview of the concept of attention in models. FREE ACCESS
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    5.  Understanding the Transformer Architecture – I
    10m 38s
    Upon completion of this video, you will be able to outline the structure of inputs to the transformer. FREE ACCESS
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    6.  Understanding the Transformer Architecture – II
    11m 12s
    After completing this video, you will be able to outline the working of transformer attention layers. FREE ACCESS
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    7.  Understanding the GPT Model
    14m 32s
    Upon completion of this video, you will be able to outline how GPT models work. FREE ACCESS
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    8.  Understanding Foundation Models
    8m 47s
    After completing this video, you will be able to recognize the importance of foundation models. FREE ACCESS
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    9.  Model Alignment – Supervised Fine-tuning (SFT)
    9m 46s
    Upon completion of this video, you will be able to provide an overview of supervised fine-tuning (SFT) for model alignment. FREE ACCESS
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    10.  Model Alignment – Reinforcement Learning from Human Feedback (RLHF)
    8m 25s
    After completing this video, you will be able to outline how reinforcement learning from human feedback (RLHF) helps improve model performance. FREE ACCESS
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    11.  Prompt Engineering and Prompt Tuning
    10m 32s
    Upon completion of this video, you will be able to contrast prompt engineering and prompt tuning. FREE ACCESS
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    12.  Course Summary
    2m 35s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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