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Practical Application of LLMs
Tom Taulli
Tom Taulli
This is a beginner-level introduction to large-language models (LLMs). This course will teach you the fundamentals of this powerful technology along with hands-on examples using the OpenAI API.
Read more
This is a beginner-level introduction to large-language models (LLMs). This course will teach you the fundamentals of this powerful technology along with hands-on examples using the OpenAI API.
Read more
This is a beginner-level introduction to large-language models (LLMs). This course will teach you the fundamentals of this powerful technology along with hands-on examples using the OpenAI API.
For businesses people and developers, understanding LLMs is critical. In this course, Practical Application of LLM's, you’ll gain the ability to understand how LLMs work and their real-world applications. First, you’ll explore the fundamentals of this powerful language, including the transformer model, prompt engineering, and training models. Next, you’ll discover how to create applications for creating content and using LangChain for automation. Finally, you’ll learn about the latest cutting-edge innovations and research. When you’re finished with this course, you’ll have the skills and knowledge of LLMs needed to understand their practical applications.
Register for this course and see more details by visiting:
OpenCourser.com/course/688cek/practical
What's inside
Syllabus
Course Overview
Understanding LLMs
Training LLMs
Real-world Applications of LLMs
Read more
Syllabus
Course Overview
Understanding LLMs
Training LLMs
Real-world Applications of LLMs
Read more
Latest Research and Advancements
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Contains practical examples using the OpenAI API and LangChain for automation
Teaches transformer models, prompt engineering, and training models, which are core concepts of LLMs
Designed for beginners in the field of large-language models
Demonstrates how to create real-world applications for creating content and using LangChain for automation
Offers insights into the latest cutting-edge innovations and research in LLMs
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Activities
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Practical Application of LLMs.
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Career center
Learners who complete Practical Application of LLMs will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers build and maintain natural language processing systems. They use natural language processing techniques to process and generate text data. This course can help Natural Language Processing Engineers build a foundation in the fundamentals of LLMs, which are a key technology in natural language processing. The course will also provide hands-on experience using the OpenAI API to develop natural language processing applications.
NLP (Natural Language Processing) Researcher
NLP (Natural Language Processing) Researcher
NLP (Natural Language Processing) Researchers develop new methods and algorithms for processing and generating text data. They use a variety of techniques, including machine learning, artificial intelligence, and linguistics. This course can help NLP Researchers build a foundation in the fundamentals of LLMs, which are a key technology in NLP. The course will also provide hands-on experience using the OpenAI API to develop NLP applications.
Computational Linguist
Computational Linguist
Computational Linguists use computational methods to study language. They develop models and algorithms to analyze and generate text data. This course can help Computational Linguists build a foundation in the fundamentals of LLMs, which are a key technology in computational linguistics. The course will also provide hands-on experience using the OpenAI API to develop computational linguistics applications.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They use data to train models that can make predictions or decisions. This course can help Machine Learning Engineers build a foundation in the fundamentals of LLMs, which are a key technology in machine learning. The course will also provide hands-on experience using the OpenAI API to develop machine learning applications.
AI Engineer
AI Engineer
AI Engineers build and maintain artificial intelligence systems. They design, develop, and implement AI solutions to solve real-world problems. This course can help build a foundation in the fundamentals of large-language models (LLMs), which are a key technology in AI. The course will also provide hands-on experience using the OpenAI API to develop AI applications.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course can help Data Scientists build a foundation in the fundamentals of LLMs, which can be used to process and generate text data. The course will also provide hands-on experience using the OpenAI API to develop data science applications.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use programming languages to create software that meets the needs of users. This course can help Software Engineers build a foundation in the fundamentals of LLMs, which can be used to improve the quality and efficiency of software development. The course will also provide hands-on experience using the OpenAI API to develop software applications.
Librarian
Librarian
Librarians help people find and use information. They manage and organize libraries, and they provide reference and research services. This course may be useful for Librarians who want to learn more about the fundamentals of LLMs, which can be used to process and generate text data. The course will also provide hands-on experience using the OpenAI API to develop library applications.
Lexicographer
Lexicographer
Lexicographers compile and edit dictionaries. They research and define words and phrases. This course may be useful for Lexicographers who want to learn more about the fundamentals of LLMs, which can be used to generate and process text data. The course will also provide hands-on experience using the OpenAI API to develop lexicography applications.
Copywriter
Copywriter
Copywriters create and maintain advertising and marketing copy. This course may be useful for Copywriters who want to learn more about the fundamentals of LLMs, which can be used to generate and process text data. The course will also provide hands-on experience using the OpenAI API to develop copywriting applications.
Journalist
Journalist
Journalists research and write news stories. They interview sources, gather information, and write articles, stories, and other content. This course may be useful for Journalists who want to learn more about the fundamentals of LLMs, which can be used to generate and process text data. The course will also provide hands-on experience using the OpenAI API to develop journalism applications.
Editor
Editor
Editors review and edit written content. They check for grammar, spelling, and style errors. This course may be useful for Editors who want to learn more about the fundamentals of LLMs, which can be used to generate and process text data. The course will also provide hands-on experience using the OpenAI API to develop editing applications.
Content Writer
Content Writer
Content Writers create and maintain web content, marketing materials, and other written content. This course may be useful for Content Writers who want to learn more about the fundamentals of LLMs, which can be used to generate and process text data. The course will also provide hands-on experience using the OpenAI API to develop content writing applications.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. They write user manuals, white papers, and other technical content. This course may be useful for Technical Writers who want to learn more about the fundamentals of LLMs, which can be used to generate and process text data. The course will also provide hands-on experience using the OpenAI API to develop technical writing applications.
Teacher
Teacher
Teachers plan and deliver lessons to students in schools and other educational settings. They use a variety of teaching methods to help students learn. This course may be useful for Teachers who want to learn more about the fundamentals of LLMs, which can be used to generate and process text data. The course will also provide hands-on experience using the OpenAI API to develop teaching applications.
For more career information including salaries, visit:
OpenCourser.com/course/688cek/practical
Reading list
We've selected eight 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
Practical Application of LLMs.
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics, including word embeddings, recurrent neural networks, convolutional neural networks, and attention mechanisms. It valuable resource for anyone who wants to learn more about the state-of-the-art in NLP.
Provides a practical guide to using LLMs for a variety of tasks, such as content creation, data analysis, and customer service. It valuable resource for anyone who wants to learn how to use LLMs in their own work.
Provides a comprehensive overview of natural language processing with Python. It covers a wide range of topics, including tokenization, stemming, lemmatization, parsing, and machine learning. It valuable resource for anyone who wants to learn more about the basics of NLP.
Provides a comprehensive overview of machine learning with TensorFlow. It covers a wide range of topics, including linear regression, logistic regression, neural networks, and deep learning. It valuable resource for anyone who wants to learn more about the basics of machine learning.
This comprehensive textbook provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language processing, and machine translation. It offers a deep understanding of the field.
Provides a historical perspective on language models, exploring their evolution and the latest advancements in the field. It offers insights into the theoretical underpinnings of LLMs.
Provides a theoretical foundation in machine learning, covering topics such as supervised and unsupervised learning, and optimization. It helps in understanding the mathematical principles behind LLMs.
This introductory guide provides a solid foundation in deep learning concepts and techniques using Python. It serves as a good starting point for those new to the field and interested in the underlying principles of LLMs.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/688cek/practical
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Time
57 hours
Level
Introductory
Via
Pluralsight
Instructor
Tom Taulli
Language
English
Good to know
Contains practical examples using the OpenAI API and LangChain for automation
Teaches transformer models, prompt engineering, and training models, which are core concepts of LLMs
Designed for beginners in the field of large-language models
Demonstrates how to create real-world applications for creating content and using LangChain for automation
Offers insights into the latest cutting-edge innovations and research in LLMs
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