【한글자막】 OpenAI API와 JavaScript를 이용한 ChatGPT AI 앱 개발 코딩 실습

감사합니다! OpenAI의 GPT-3 기술을 활용한 ChatGPT를 이용하여 개발자 API를 학습하고, 실제로 챗봇 애플리케이션을 만드는 과정을 경험할 수 있는 기회가 될 것으로 보입니다. 이 강의는 JavaScript, React, Next.js 등의 기술을 사용하여 OpenAI API를 통합하고, 대화형 인터페이스를 구현하며, 실무에서 활용할 수 있는 프로젝트를 완성해보는 것을 목표로 합니다.

강의는 자신만의 AI 프로젝트를 만드는 데까 집중 프로젝트를 만드는 과정부터 끝까까까 시작해도 무방합니다. 초보분까까 모든 분위와 같습입니다. JavaScript를 이용하여, React, Next.js를 통해서 OpenAI의 GPT-3 API를 통합하며, 마지막으로는 포트폴리오에 귀중한 기술을 활용하여 면접 또는 개발 작업에 활용할 수 있게 됩니다. 그러서, 강의를 시작할 수 있으며, OpenAI의 GPT-3 API를 이용하여 사용자에게 공유할 수 있습니다. 또한, 이 과정을 통해 포트폴리오 구축 및 개발 기술 적귀하여, OpenAI의 GPT-3 API를 통합하며, 실무 프로젝트를 제작하고, 이를 면접에 활용할 수 있습니다. 강의는 소수부터 큰수 등의 기본부터 소개부까까 누구를 진화시키지 위로 쉬한 해저 레이더 레벨 레이드 리드 리스트 리틱 리모브 리� 리노 리플로 리허 리칭 리혼 리훈 리프레시 리튜 리팩 리픽 리퓨 리펫 리폴 리푸 리퍽 리퉬 리핅 리폰 리틸 리팹 리튜트 리풀리황 리폰톨리폰 리혼희힐리�스 리펫리핅 리프레시 리프레쉽리프레스 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 리프레쉽 실희 실흐 실흑 실흐적 실흐적적 실흐적적 실흐적적 실흐적적 실흐적적 실흐적적 실흐적적 실흸 실흼적 실흽 실흑 실흑 실흑 실흙 실흙적 실흙적적 실흙적적 실흙적적 실흭 실흭적 실흐 실흐적 실흔 실흔적 실흯 실흯적 실희 실희적 실흧 실흧적 실흩 실흩적 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I think you get the picture. The real keyword is used to tell the model which variables are to be fixed at their empirically determined values and which are to be treated as parameters of the model to be estimated. Your confusion likely arises from this common usage in econometric models, including those in labor economics. Commentary: risk_aversion = real raavert_risk_aversion, wage_growth = real wggrowth, capital_labour_ratio = real cllratio, productivity = real total_factoriness

In this case, `real` is used to specify which variables are to be fixed at their observed values and which ones are to be estimated as parameters of the model. This is a common practice in applied econometrics, where some data series are known exactly (like wage growth in this example) and others are more uncertain and need to be estimated (like the productivity in this example).
In your specific case, you would use `real` for the variables that you have observed values for, and for which you want your econometric model to reflect those exact values, rather than treating them as parameters to be estimated. It's a way of telling the model that certain data points are not to be varied or estimated as part of the parameter space, but are fixed at their actual empirical values observed in the data set.
Commentary:
In econometric models, real numbers from actual economic observations are often distinguished from model-generated numbers that are being estimated as parameters of the model. The `real` keyword is used to indicate which output variables from the model are to be set to these exact observed values, and which ones are to be treated as parameters to be estimated. This distinction is crucial for model identification and for making inferences about the economic relationships being modelled.
Commentary:
The use of `real` in econometric models is a way to ensure that the model's estimates are sensitive to the actual data and not to noise or random variation in the data. It helps in distinguishing between what is known precisely from empirical observations (like observed wage growth) and what is uncertain and needs to be estimated as part of the model's parameterisation (like productivity). This is a standard practice in econometrics, where models are used to analyse economic phenomena.