Generating New Recipes using GPT-2
Clean and preprocess text data for modeling
Create datasets for large-scale language generation
Fine-tune large-scale language model on small and niche task of generating recipes
In this 2 hour long project, you will learn how to preprocess a text dataset comprising recipes, and split it into a training and validation set. You will learn how to use the HuggingFace library to fine-tune a deep, generative model, and specifically how to train such a model on Google Colab. Finally, you will learn how to use GPT-2 effectively to create realistic and unique recipes from lists of ingredients based on the aforementioned dataset. This project aims to teach you how to fine-tune a large-scale model, and the sheer magnitude of resources it takes for these models to learn. You will also learn about knowledge distillation and its efficacy in use cases such as this one. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
您要培養的技能
分步進行學習
在與您的工作區一起在分屏中播放的視頻中，您的授課教師將指導您完成每個步驟：
Introduction to the task and demo
Exploratory data analysis and visualizations
Dataset preparation
GPT-2 theory and related machine learning concepts
Model training on Google Colab
Evaluating model performance empirically
指導項目工作原理
您的工作空間就是瀏覽器中的雲桌面，無需下載
在分屏視頻中，您的授課教師會為您提供分步指導
常見問題
常見問題
如果我購買指導 項目，會得到什麼？
指導 項目 可在台式設備和移動設備上學習嗎？
指導 項目 的講師是誰？
我能在完成指導 項目 後從中下載作品嗎？
我能夠退款嗎？退款政策是如何規定的？
有助學金嗎？
我能旁聽指導 項目 並免費觀看視頻部分嗎？
我需要具備多少經驗才能做這個指導 項目？
我能直接通過 Web 瀏覽器來完成此指導 項目，而不必安裝特殊軟件嗎？
指導 項目 的學習體驗如何？
還有其他問題嗎？請訪問 學生幫助中心。
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