
모두를 위한 대규모 언어 모델 LLM(Large Language Model) Part 1 - Llama 2 Fine-Tuning 해보기
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LLM(Large Language Model)의 기초 개념부터 고성능 LLM인 Llama 2 모델을 내가 원하는 데이터셋에 Fine-Tuning하는 방법까지 차근차근 학습합니다.
중급이상
LLM, Llama, 딥러닝
A course to learn deep learning paper implementation skills, implementing the GPT-1 (Improving Language Understanding by Generative Pre-Training) paper from scratch with TensorFlow 2.0.

How to Read Deep Learning Papers
How to implement Deep Learning papers
Detailed Understanding of GPT (Generative Pre-trained Transformer) Model Structure
GPT (Generative Pre-trained Transformer) Model Background
Coding with TensorFlow 2.0
An essential skill for deep learning researchers: the ability to implement the latest research papers!
Learn with GPT implementation 😀
When hiring deep learning researchers, many companies give preference to those with experience implementing the latest papers .
Gain experience implementing the latest papers by implementing the GPT-1 (Improving Language Understanding by Generative Pre-Training) paper yourself.
We will read the GPT-1 paper (Improving Language Understanding by Generative Pre-Training) and implement the GPT-1 model from scratch using TensorFlow 2.0 .
👋 This course requires prior knowledge of TensorFlow 2.0 and natural language processing (NLP) . Be sure to take the courses below first or have equivalent knowledge before taking this course.
Q. What are the benefits of experiencing implementing deep learning papers?
Who is this course right for?
Those wishing to develop deep learning paper reading and implementation skills.
Individuals aspiring to deep learning research roles.
Those who wish to conduct AI/Deep Learning research
AI Graduate School Preparers
Need to know before starting?
Python experience
Prior Course: [Introduction to Deep Learning Natural Language Processing with Examples: NLP with TensorFlow - From RNN to BERT] Course Experience
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30 lectures ∙ (4hr 27min)
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$84.70
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