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Generating New Recipes using GPT-2
Ari Anastassiou
Ari Anastassiou
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...
Read more
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...
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/kspde3/generating
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Assumes learners are familiar with the code and concepts behind generative language models
Designed for individuals with some background in natural language processing and machine learning
Helps learners refine their ability to develop and evaluate AI-powered language models
Instructs learners in the practical application of deep generative models
Provides hands-on experience in fine-tuning a large-scale model
Covers the advantages and potential limitations of knowledge distillation techniques
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Reviews summary
Disappointing gpt-2 recipe project
This hands-on project aims to teach students how to fine-tune GPT-2 to generate new recipes. However, many students expressed dissatisfaction with the outdated content and a lack of resources and support.
Knowledgeable and responsive instructor
"The instructor was really terrific."
Project pace is too fast for beginners
"More focus on completion then explanation. Bit fast for learners."
Generated recipes often inaccurate
"The resulting trained system tends to hallucinate a lot."
Content is outdated and contains errors
"The content was outdated...the instructor had a lot of syntax errors during the recordings."
Resources are incomplete and unavailable after course completion
"Thank you for your efforts but resources of this course are not available once the course have completed!!"
"Incomplete resources, a lot of people unable to finish the project because the lecturer does not include all the files needed to execute them!"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Generating New Recipes using GPT-2 with these
activities:
Review Basic Programming Concepts
Show steps
Refresh your understanding of fundamental programming concepts like variables, data types, loops, and conditionals to build a strong foundation for this course.
Browse courses on
Python Programming
Show steps
-
Go through online tutorials on basic programming concepts.
-
Solve coding challenges on platforms like LeetCode or HackerRank.
-
Review lecture notes or textbooks from previous programming courses.
Organize and enhance your course notes
Show steps
Consolidate and improve your understanding of the course material by organizing and enhancing your notes.
Show steps
-
Review your existing notes and identify areas that need improvement.
-
Add additional details, explanations, and examples to enhance your notes.
-
Organize your notes logically to make them easy to navigate and reference.
Connect with Experts in Natural Language Processing
Show steps
Seek guidance from experienced professionals in natural language processing. This will provide you with valuable insights and support throughout your learning journey.
Browse courses on
Natural Language Processing
Show steps
-
Attend industry events and meetups.
-
Reach out to professors or researchers in the field.
-
Join online communities and forums.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Explore HuggingFace's GPT-2 library
Show steps
Become familiar with the HuggingFace's GPT-2 library to enhance your understanding of its functionalities.
Browse courses on
HuggingFace
Show steps
-
Get an overview of the HuggingFace website and its resources.
-
Navigate the documentation for the GPT-2 library.
-
Explore the examples provided by HuggingFace for using the GPT-2 library.
Complete Introductory GPT-2 Tutorials
Show steps
Follow guided tutorials to gain hands-on experience with GPT-2, the language model used in this course. This will familiarize you with its capabilities and limitations.
Browse courses on
GPT-2
Show steps
-
Find beginner-friendly tutorials on using GPT-2.
-
Follow the tutorials step-by-step, experimenting with different inputs and parameters.
-
Explore the GPT-2 API documentation to understand its functionality.
Participate in Study Groups with Classmates
Show steps
Engage in regular study sessions with classmates to discuss course concepts, share insights, and work through problems together.
Browse courses on
Collaboration
Show steps
-
Form or join a study group with classmates.
-
Set regular meeting times and stick to them.
-
Take turns leading discussions and presenting topics.
Practice fine-tuning GPT-2 on text datasets
Show steps
Practice fine-tuning GPT-2 on text datasets to solidify your understanding of the model's capabilities.
Show steps
-
Find a suitable text dataset.
-
Preprocess the dataset to make it suitable for training GPT-2.
-
Set up a training environment for GPT-2.
-
Fine-tune the model on the prepared dataset.
-
Evaluate the performance of the fine-tuned model.
Write a Summary of GPT-2 Architecture
Show steps
Create a written summary of the architecture and functioning of GPT-2. This will help you solidify your understanding of the model's inner workings.
Browse courses on
GPT-2
Show steps
-
Research GPT-2's architecture and technical details.
-
Organize your findings into a coherent summary.
-
Write a clear and concise explanation of GPT-2's architecture.
Practice Fine-tuning GPT-2 on Custom Data
Show steps
Practice fine-tuning GPT-2 on your own custom dataset. This will allow you to apply the techniques learned in the course to a real-world scenario.
Browse courses on
GPT-2
Show steps
-
Prepare a custom dataset for fine-tuning.
-
Use the HuggingFace Transformers library to fine-tune GPT-2 on your dataset.
-
Experiment with different hyperparameters and evaluate the performance of your fine-tuned model.
Develop a GPT-2-Based Recipe Generator
Show steps
Create a functional recipe generator using GPT-2. This project will challenge you to combine your knowledge of GPT-2 with practical software development skills.
Browse courses on
GPT-2
Show steps
-
Design the architecture and functionality of your recipe generator.
-
Implement the GPT-2 model and integrate it into your application.
-
Develop a user interface for your recipe generator.
-
Test and refine your recipe generator based on user feedback.
Review Basic Programming Concepts
Show steps
Refresh your understanding of fundamental programming concepts like variables, data types, loops, and conditionals to build a strong foundation for this course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Go through online tutorials on basic programming concepts.
- Solve coding challenges on platforms like LeetCode or HackerRank.
- Review lecture notes or textbooks from previous programming courses.
Organize and enhance your course notes
Show steps
Consolidate and improve your understanding of the course material by organizing and enhancing your notes.
Show steps
Show steps
Show steps
- Review your existing notes and identify areas that need improvement.
- Add additional details, explanations, and examples to enhance your notes.
- Organize your notes logically to make them easy to navigate and reference.
Connect with Experts in Natural Language Processing
Show steps
Seek guidance from experienced professionals in natural language processing. This will provide you with valuable insights and support throughout your learning journey.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Attend industry events and meetups.
- Reach out to professors or researchers in the field.
- Join online communities and forums.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Explore HuggingFace's GPT-2 library
Show steps
Become familiar with the HuggingFace's GPT-2 library to enhance your understanding of its functionalities.
Browse courses on
HuggingFace
Show steps
Show steps
Show steps
- Get an overview of the HuggingFace website and its resources.
- Navigate the documentation for the GPT-2 library.
- Explore the examples provided by HuggingFace for using the GPT-2 library.
Complete Introductory GPT-2 Tutorials
Show steps
Follow guided tutorials to gain hands-on experience with GPT-2, the language model used in this course. This will familiarize you with its capabilities and limitations.
Browse courses on
GPT-2
Show steps
Show steps
Show steps
- Find beginner-friendly tutorials on using GPT-2.
- Follow the tutorials step-by-step, experimenting with different inputs and parameters.
- Explore the GPT-2 API documentation to understand its functionality.
Participate in Study Groups with Classmates
Show steps
Engage in regular study sessions with classmates to discuss course concepts, share insights, and work through problems together.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Form or join a study group with classmates.
- Set regular meeting times and stick to them.
- Take turns leading discussions and presenting topics.
Practice fine-tuning GPT-2 on text datasets
Show steps
Practice fine-tuning GPT-2 on text datasets to solidify your understanding of the model's capabilities.
Show steps
Show steps
Show steps
- Find a suitable text dataset.
- Preprocess the dataset to make it suitable for training GPT-2.
- Set up a training environment for GPT-2.
- Fine-tune the model on the prepared dataset.
- Evaluate the performance of the fine-tuned model.
Write a Summary of GPT-2 Architecture
Show steps
Create a written summary of the architecture and functioning of GPT-2. This will help you solidify your understanding of the model's inner workings.
Browse courses on
GPT-2
Show steps
Show steps
Show steps
- Research GPT-2's architecture and technical details.
- Organize your findings into a coherent summary.
- Write a clear and concise explanation of GPT-2's architecture.
Practice Fine-tuning GPT-2 on Custom Data
Show steps
Practice fine-tuning GPT-2 on your own custom dataset. This will allow you to apply the techniques learned in the course to a real-world scenario.
Browse courses on
GPT-2
Show steps
Show steps
Show steps
- Prepare a custom dataset for fine-tuning.
- Use the HuggingFace Transformers library to fine-tune GPT-2 on your dataset.
- Experiment with different hyperparameters and evaluate the performance of your fine-tuned model.
Develop a GPT-2-Based Recipe Generator
Show steps
Create a functional recipe generator using GPT-2. This project will challenge you to combine your knowledge of GPT-2 with practical software development skills.
Browse courses on
GPT-2
Show steps
Show steps
Show steps
- Design the architecture and functionality of your recipe generator.
- Implement the GPT-2 model and integrate it into your application.
- Develop a user interface for your recipe generator.
- Test and refine your recipe generator based on user feedback.
Career center
Learners who complete Generating New Recipes using GPT-2 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists apply advanced computational techniques to analyze and interpret large datasets. This course can help you develop the skills needed to preprocess and analyze text data, which is a crucial skill for Data Scientists. Additionally, the knowledge you gain about fine-tuning large-scale models can be applied to other types of data, such as images and audio.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. This course can help you develop the skills needed to train and fine-tune large-scale models, which is a key part of machine learning engineering. Additionally, you will learn about knowledge distillation, which is a technique for reducing the size of machine learning models without sacrificing accuracy.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and maintain software that can understand and generate human language. This course can help you develop the skills needed to preprocess and analyze text data, which is a fundamental part of natural language processing. Additionally, you will learn about fine-tuning large-scale models, which can be used to build state-of-the-art natural language processing systems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in software engineering. Additionally, you will learn about fine-tuning large-scale models, which can be used to build intelligent software applications.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in data analysis. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help businesses make better decisions.
Business Analyst
Business Analyst
Business Analysts help businesses identify and solve problems. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in business analysis. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help businesses make better decisions.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in product management. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help product managers make better decisions about product development.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the development and execution of marketing campaigns. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in marketing. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help marketing managers make better decisions about marketing campaigns.
Sales Manager
Sales Manager
Sales Managers are responsible for the development and execution of sales strategies. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in sales. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help sales managers make better decisions about sales strategies.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with a company's products or services. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in customer success management. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help customer success managers make better decisions about how to support customers.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a company. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in operations management. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help operations managers make better decisions about how to operate the company.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for the recruitment, hiring, and management of employees. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in human resources management. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help human resources managers make better decisions about how to recruit and hire employees.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice to individuals and businesses. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in financial analysis. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help financial analysts make better decisions about investments.
Consultant
Consultant
Consultants provide advice to businesses on how to improve their performance.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. This course can help you develop the skills needed to preprocess and analyze text data, which is a common task in entrepreneurship. Additionally, you will learn about fine-tuning large-scale models, which can be used to build predictive models that can help entrepreneurs make better decisions about their businesses.
For more career information including salaries, visit:
OpenCourser.com/course/kspde3/generating
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Generating New Recipes using GPT-2.
Provides a comprehensive overview of deep learning for NLP, covering a wide range of topics, including word embeddings, recurrent neural networks, and transformers. It valuable resource for anyone interested in learning more about this field.
Provides a comprehensive overview of generative deep learning, including topics such as generative adversarial networks (GANs), variational autoencoders (VAEs), and reinforcement learning for generative models, with a focus on teaching machine to generate creative content such as images, music, and text.
Explores the relationship between language models and human language, providing insights into the nature of language and how it is processed by the human brain.
Provides a practical introduction to deep learning for coders, using the Fastai library and PyTorch framework.
Provides a comprehensive introduction to machine learning, using the Scikit-Learn, Keras, and TensorFlow libraries.
Provides a practical introduction to deep learning, using the Keras library.
Provides a comprehensive introduction to natural language processing, using the NLTK library.
Provides a comprehensive introduction to speech and language processing, covering topics such as speech recognition, natural language understanding, and dialogue systems.
Provides a comprehensive introduction to the statistical foundations of natural language processing, covering topics such as probability theory, information theory, and machine learning.
Provides a comprehensive introduction to probabilistic graphical models, which are a powerful tool for representing and reasoning about complex systems.
Provides a practical introduction to machine learning for programmers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kspde3/generating
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ari Anastassiou
Good to know
Assumes learners are familiar with the code and concepts behind generative language models
Designed for individuals with some background in natural language processing and machine learning
Helps learners refine their ability to develop and evaluate AI-powered language models
Instructs learners in the practical application of deep generative models
Provides hands-on experience in fine-tuning a large-scale model
Covers the advantages and potential limitations of knowledge distillation techniques
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