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Finetuning Large Language Models
Sharon Zhou
Sharon Zhou
In this short course, you’ll learn essential finetuning concepts and how to train a large language model using your own data. You’ll be equipped to incorporate the latest techniques to optimize your model and produce transformative results.
Read more
In this short course, you’ll learn essential finetuning concepts and how to train a large language model using your own data. You’ll be equipped to incorporate the latest techniques to optimize your model and produce transformative results.
Read more
In this short course, you’ll learn essential finetuning concepts and how to train a large language model using your own data. You’ll be equipped to incorporate the latest techniques to optimize your model and produce transformative results.
When you complete this course, you will be able to:
Understand when to apply finetuning on LLMs
Prepare your data for finetuning
Train and evaluate an LLM on your data
With finetuning, you’re able to take your own data to train the model on it, and update the weights of the neural nets in the LLM, changing the model compared to other methods like prompt engineering and Retrieval Augmented Generation. Finetuning allows the model to learn style, form, and can update the model with new knowledge to improve results.
Register for this course and see more details by visiting:
OpenCourser.com/course/8uafn6/finetuning
What's inside
Syllabus
Project Overview
Join our new short course, Finetuning Large Language Models! Learn from Sharon Zhou, Co-Founder and CEO of Lamini, and instructor for the GANs Specialization and How Diffusion Models Work. When you complete this course, you will be able to:(1) Understand when to apply finetuning on LLMs.(2) Prepare your data for finetuning.(3) Train and evaluate an LLM on your data.With finetuning, you’re able to take your own data to train the model on it, and update the weights of the neural nets in the LLM, changing the model compared to other methods like prompt engineering and Retrieval Augmented Generation. Finetuning allows the model to learn style, form, and can update the model with new knowledge to improve results.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills essential to working in AI research and development
Suitable for advanced learners with experience in AI and machine learning
Taught by experts in the field with real-world experience
Covers the latest advancements in LLM finetuning
Provides hands-on experience through practical exercises and projects
Career-oriented course that prepares learners for roles in AI and machine learning
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Finetuning Large Language Models.
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Career center
Learners who complete Finetuning Large Language Models will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design, build and test systems that allow computers to understand and generate human language. They may create models that understand and respond to questions posed in natural language, that translate languages, or that extract information from text. This course in large language models will help the Natural Language Processing Engineer to understand the theory behind a trending language model. The course also provides hands-on experience in fine-tuning a large language model using one's own data. As a Natural Language Processing Engineer may use a large language model to complete their work, this course may prove helpful in advancing their career.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. Machine learning models are used to make predictions or recommendations based on data. This course in large language models will help the Machine Learning Engineer to understand the theory behind a trending language model. The course also provides hands-on experience in fine-tuning a large language model using one's own data. As a Machine Learning Engineer may use a large language model to complete their work, this course may prove helpful in advancing their career.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make predictions. They may use data to identify trends, develop models, or create visualizations. This course in large language models will help the Data Scientist to understand the theory behind a trending language model. The course also provides hands-on experience in fine-tuning a large language model using one's own data. As a Data Scientist may use a large language model to complete their work, this course may prove helpful in advancing their career.
Software Engineer
Software Engineer
Software Engineers design, build and test software systems. They may work on a variety of projects, such as developing new features for existing software, or creating new software applications. This course in large language models may be helpful to the Software Engineer who wishes to incorporate large language models into their software. As software becomes more sophisticated, an understanding of natural language processing is increasingly helpful in order to develop robust and user-friendly applications.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course in large language models may be helpful for the Product Manager who wishes to understand the potential of large language models. Large language models can be used to generate new product ideas, to improve customer service, or to create personalized marketing campaigns. An understanding of large language models can help the Product Manager to make better decisions about how to use these technologies to improve their products.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They may work in a variety of industries, such as finance, insurance, and healthcare. This course in large language models may be helpful to the Quantitative Analyst who wishes to use large language models to improve their models. Large language models can be used to extract information from text data, to generate new hypotheses, or to identify patterns in data.
Business Analyst
Business Analyst
Business Analysts use data to solve problems and improve business processes. They may work with stakeholders to identify problems, develop solutions, and implement changes. This course in large language models may be helpful to the Business Analyst who wishes to use large language models to improve their work. Large language models can be used to analyze customer feedback, to identify trends in data, or to generate new ideas.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user interfaces for websites, apps, and other digital products. They work to ensure that user interfaces are easy to use and enjoyable. This course in large language models may be helpful to the User Experience Designer who wishes to use large language models to improve the user experience of their products. Large language models can be used to generate natural language text, to translate languages, or to answer questions. An understanding of large language models can help the User Experience Designer to create better user interfaces and improve the overall user experience.
Content Writer
Content Writer
Content Writers create written content for websites, blogs, and other digital platforms. They may write articles, blog posts, social media posts, or other types of content. This course in large language models may be helpful to the Content Writer who wishes to use large language models to improve their writing. Large language models can be used to generate new content, to edit and improve existing content, or to translate languages.
Technical Writer
Technical Writer
Technical Writers create documentation for software, hardware, and other technical products. They may write user manuals, technical reports, or other types of documentation. This course in large language models may be helpful to the Technical Writer who wishes to use large language models to improve their writing. Large language models can be used to generate new content, to edit and improve existing content, or to translate languages.
Copywriter
Copywriter
Copywriters create written content for marketing and advertising campaigns. They may write ad copy, brochures, website content, or other types of marketing materials. This course in large language models may be helpful to the Copywriter who wishes to use large language models to improve their writing. Large language models can be used to generate new content, to edit and improve existing content, or to translate languages.
Editor
Editor
Editors review, edit, and proofread written content. They may work for newspapers, magazines, websites, or other publishers. This course in large language models may be helpful to the Editor who wishes to use large language models to improve their editing. Large language models can be used to check for grammar and spelling errors, to identify plagiarism, or to suggest improvements to written content.
Journalist
Journalist
Journalists write and report on news stories for newspapers, magazines, websites, or other media outlets. This course in large language models may be helpful to the Journalist who wishes to use large language models to improve their reporting. Large language models can be used to gather information, to check facts, or to generate new story ideas.
Librarian
Librarian
Librarians help people find and access information. They may work in public libraries, school libraries, or other types of libraries. This course in large language models may be helpful to the Librarian who wishes to use large language models to improve their work. Large language models can be used to catalog books, to answer reference questions, or to create personalized recommendations for library users.
Teacher
Teacher
Teachers educate students in a variety of subjects. They may work in public schools, private schools, or other educational settings. This course in large language models may be helpful to the Teacher who wishes to use large language models to improve their teaching. Large language models can be used to create lesson plans, to answer student questions, or to provide personalized feedback to students.
For more career information including salaries, visit:
OpenCourser.com/course/8uafn6/finetuning
Reading list
We've selected ten 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
Finetuning Large Language Models.
Provides a comprehensive overview of natural language processing with Python. It covers a wide range of topics, including text processing, machine learning, and natural language understanding.
Provides a comprehensive overview of natural language understanding. It covers a wide range of topics, including syntax, semantics, and pragmatics.
Provides a practical guide to natural language processing. It covers a wide range of topics, including text classification, named entity recognition, and machine translation.
Provides a practical guide to Keras for deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK). It covers a wide range of topics, including text processing, machine learning, and natural language understanding.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, including data preparation, feature engineering, model selection, and evaluation.
Save
Comprehensive reference on deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive introduction to deep learning. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including linear regression, logistic regression, and decision trees.
Provides a comprehensive introduction to pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and Bayesian methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8uafn6/finetuning
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Sharon Zhou
Language
English
Good to know
Develops skills essential to working in AI research and development
Suitable for advanced learners with experience in AI and machine learning
Taught by experts in the field with real-world experience
Covers the latest advancements in LLM finetuning
Provides hands-on experience through practical exercises and projects
Career-oriented course that prepares learners for roles in AI and machine learning
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