Introduction to LLMs and the generative AI project lifecycle

1. Course Introduction - https://www.coursera.org/learn/generative-ai-with-llms/lecture/9uWab/course-introduction

Course Introduction - Week 1 | Coursera

PEFT - Parameter Efficient Fine Tuning

The course emphasizes the power of LLMs as a developer tool, highlighting their ability to significantly reduce the time required to build machine learning and AI applications. It covers technical details like model training, fine-tuning, and the generative AI project life cycle framework.

The course is covering topics such as understanding the transformer architecture, training LLMs, in-context learning, prompt engineering, fine-tuning models, aligning model output with human values, and handling toxicity. Each week includes hands-on labs where learners can apply the concepts in an AWS environment.

 

2. Inroduction - Week 1 - https://www.coursera.org/learn/generative-ai-with-llms/lecture/s0ClC/introduction-week-1

Introduction - Week 1 - Week 1 | Coursera

Deep Dive into Transformer Networks: The week begins with a deep dive into how transformer networks work. The instructors acknowledge that transformers can seem complex, but they aim to provide learners with an intuitive understanding of key concepts, such as self-attention and multi-headed attention. The goal is to demystify the transformer architecture and explain why it's so effective.

Scale and Parallelism: They mention that one of the reasons for the success of transformers is their ability to work at scale and in parallel, making them suitable for modern GPUs.

Foundation for Other Modalities: The discussion extends beyond text to highlight that the fundamental transformer architecture is now being used in various applications, including vision transformers. Understanding transformers is becoming a crucial building block for various machine learning domains.

Generative AI Project Lifecycle: The second major topic of the week is the Generative AI project lifecycle. This framework guides learners through the stages and decisions involved in developing generative AI applications.

Choosing the Right Model: They emphasize the importance of selecting the right model for a project, whether it's an off-the-shelf model or one that needs pre-training and fine-tuning. Model sizing is also discussed, with consideration for when a smaller model might be sufficient for a specific task.

Optimizing for Specific Use Cases: The instructors stress that large language models with hundreds of billions of parameters are beneficial when you need general knowledge. Still, for more specific use cases, smaller models can often provide excellent results.

 

트랜스포머 네트워크의 심층 분석: 이 주에는 트랜스포머 네트워크가 어떻게 작동하는지에 대한 심층 분석이 진행됩니다. 강사들은 트랜스포머가 복잡하게 보일 수 있다고 인정하면서도 핵심 개념인 self-attention과 multi-headed attention을 학습자들에게 직관적으로 이해시키는 것을 목표로 삼습니다. 트랜스포머 아키텍처의 실제 작동 원리를 해석하고 왜 그렇게 효과적인지 설명하는 것이 목표입니다.

규모와 병렬 처리: 강사들은 트랜스포머의 성공 이유 중 하나로 트랜스포머가 대규모 및 병렬 처리에서 작동할 수 있는 능력을 언급하며, 이로 인해 현대 GPU에 적합하다고 설명합니다.

다른 모달리티를 위한 기반: 이 토론은 텍스트 이외의 다양한 응용 프로그램, 비전 트랜스포머를 포함하여 기본 트랜스포머 아키텍처가 현재 여러 기계 학습 도메인에서 중요한 구성 요소로 사용되고 있다는 점을 강조합니다. 트랜스포머를 이해하는 것이 다양한 기계 학습 분야의 중요한 기반 요소가 되고 있는 것입니다.

생성적 AI 프로젝트 라이프사이클: 이 주의 두 번째 주요 주제는 생성적 AI 프로젝트 라이프사이클입니다. 이 프레임워크는 생성적 AI 애플리케이션을 개발하는 데 관련된 단계와 결정을 학습자들에게 안내합니다.

적절한 모델 선택: 강사들은 프로젝트에 적합한 모델을 선택하는 중요성을 강조하며, 그것이 현장에서 사용 가능한 모델인지 또는 미세 조정 및 세부 조정이 필요한 모델인지에 대해 설명합니다. 모델 크기도 특정 작업에는 더 작은 모델이 충분할 수 있는 경우를 고려하여 논의됩니다.

특정 사용 사례에 최적화: 강사들은 수백 억 개의 매개변수를 가진 대규모 언어 모델이 일반적인 지식이 필요한 경우에 유용하다고 강조하면서도, 더 구체적인 사용 사례에 대해서는 더 작은 모델이 종종 훌륭한 결과를 제공할 수 있다고 강조합니다.