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Tom Yeh
This introductory course offers a comprehensive exploration of Generative AI, including Transformers, ChatGPT for generating text, and Generative Adversarial Networks (GANs), the Diffusion Model for generating images. By the end of this course, you will gain a basic understanding of these Generative AI models, their underlying theories, and practical considerations. You will build a solid foundation and become ready to dive deeper into more advanced topics in the next course.
Register for this course and see more details by visiting:
OpenCourser.com/course/cvp0l1/introduction
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What's inside
Syllabus
Generative Model
Welcome to "Introduction to Generative AI." This first week, you will learn about the basics of a Generative Model.
Generative Adversarial Network (GAN)
Read more
Syllabus
Generative Model
Welcome to "Introduction to Generative AI." This first week, you will learn about the basics of a Generative Model.
Generative Adversarial Network (GAN)
Read more
This week, you will learn about the Generative Adversarial Network, the first successful deep learning approach to generating realistic looking images, which started a new wave of generative AI research.
Language Model
This week, you will learn about Language Models for Generative AI, including Transformer and ChatGPT.
Image Model
This week, you will learn about Image Models for Generative AI, from basic probabilistic models to state-of-the art Diffusion Models.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces generative adversarial networks (GANs) and their use in generating realistic images
Covers language models and their applications in generative AI, such as ChatGPT
Provides a comprehensive overview of generative AI, including its underlying theories and practical considerations
Designed for beginners with little to no prior knowledge of generative AI
Taught by instructors, Tom Yeh, who are recognized for their work in generative AI
Part of a series of courses on generative AI, providing a structured learning path for learners interested in the field
Save this course
Save Introduction to Generative AI to your list so you can find it easily later:
Save
Save
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 Introduction to Generative AI with these
activities:
Review Machine Learning Basics
Show steps
Refresh your knowledge of foundational Machine Learning concepts to strengthen your understanding of Generative AI models.
Browse courses on
Machine Learning Basics
Show steps
-
Review fundamental concepts of supervised and unsupervised learning.
-
Summarize popular machine learning algorithms, such as linear regression and decision trees.
-
Practice applying these concepts to simple datasets.
Organize Course Materials for Future Reference
Show steps
Keep your notes, assignments, and resources organized to facilitate easy review and retention of course material.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Sort materials into logical categories (e.g., lecture notes, assignments, readings).
-
Review and summarize key concepts regularly.
Data Exploration and Preparation Practice
Show steps
Practice data wrangling techniques to improve your ability to prepare datasets for Generative AI models.
Show steps
-
Load and clean datasets using Python libraries (e.g., Pandas, NumPy).
-
Explore data distributions, identify outliers, and handle missing values.
-
Split datasets into training and testing sets using industry best practices.
-
Apply data transformations and feature engineering techniques.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Tutorial on Transformer Architecture for LSTMs
Show steps
Explore Transformers' underlying architecture to gain a deeper understanding of text-based Generative AI models like ChatGPT.
Show steps
-
Follow a guided tutorial on Transformer architecture and its components.
-
Learn about the attention mechanism and its role in language processing.
-
Compare LSTM and Transformer models and understand their relative strengths.
Generative AI Model Evaluation Techniques
Show steps
Practice evaluating the performance of Generative AI models to enhance your ability to assess their effectiveness.
Show steps
-
Learn about different evaluation metrics for text-based and image-based models.
-
Apply these metrics to assess the quality of generated text and images.
-
Compare the performance of different Generative AI models using these metrics.
Image Generation with Diffusion Models
Show steps
Build a Diffusion Model to generate novel images, solidifying your understanding of image-based Generative AI models.
Show steps
-
Implement a Diffusion Model using a deep learning framework (e.g., PyTorch).
-
Experiment with different hyperparameters to optimize image quality.
-
Fine-tune the model on a specific dataset to generate realistic images.
Connect with AI Professionals
Show steps
Network with AI professionals to gain insights, explore career paths, and enhance your understanding of Generative AI.
Show steps
-
Attend industry events and conferences.
-
Reach out to AI researchers and practitioners on LinkedIn.
-
Join online communities and forums related to Generative AI.
Create a Blog Post on Generative AI Applications
Show steps
Demonstrate your understanding of Generative AI by creating a blog post that explores its applications across various industries.
Show steps
-
Research and identify real-world applications of Generative AI.
-
Write a blog post that clearly explains these applications and their potential impact.
-
Share your blog post on social media and engage with readers.
Review Machine Learning Basics
Show steps
Refresh your knowledge of foundational Machine Learning concepts to strengthen your understanding of Generative AI models.
Browse courses on
Machine Learning Basics
Show steps
Show steps
Show steps
- Review fundamental concepts of supervised and unsupervised learning.
- Summarize popular machine learning algorithms, such as linear regression and decision trees.
- Practice applying these concepts to simple datasets.
Organize Course Materials for Future Reference
Show steps
Keep your notes, assignments, and resources organized to facilitate easy review and retention of course material.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Sort materials into logical categories (e.g., lecture notes, assignments, readings).
- Review and summarize key concepts regularly.
Data Exploration and Preparation Practice
Show steps
Practice data wrangling techniques to improve your ability to prepare datasets for Generative AI models.
Show steps
Show steps
Show steps
- Load and clean datasets using Python libraries (e.g., Pandas, NumPy).
- Explore data distributions, identify outliers, and handle missing values.
- Split datasets into training and testing sets using industry best practices.
- Apply data transformations and feature engineering techniques.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Tutorial on Transformer Architecture for LSTMs
Show steps
Explore Transformers' underlying architecture to gain a deeper understanding of text-based Generative AI models like ChatGPT.
Show steps
Show steps
Show steps
- Follow a guided tutorial on Transformer architecture and its components.
- Learn about the attention mechanism and its role in language processing.
- Compare LSTM and Transformer models and understand their relative strengths.
Generative AI Model Evaluation Techniques
Show steps
Practice evaluating the performance of Generative AI models to enhance your ability to assess their effectiveness.
Show steps
Show steps
Show steps
- Learn about different evaluation metrics for text-based and image-based models.
- Apply these metrics to assess the quality of generated text and images.
- Compare the performance of different Generative AI models using these metrics.
Image Generation with Diffusion Models
Show steps
Build a Diffusion Model to generate novel images, solidifying your understanding of image-based Generative AI models.
Show steps
Show steps
Show steps
- Implement a Diffusion Model using a deep learning framework (e.g., PyTorch).
- Experiment with different hyperparameters to optimize image quality.
- Fine-tune the model on a specific dataset to generate realistic images.
Connect with AI Professionals
Show steps
Network with AI professionals to gain insights, explore career paths, and enhance your understanding of Generative AI.
Show steps
Show steps
Show steps
- Attend industry events and conferences.
- Reach out to AI researchers and practitioners on LinkedIn.
- Join online communities and forums related to Generative AI.
Create a Blog Post on Generative AI Applications
Show steps
Demonstrate your understanding of Generative AI by creating a blog post that explores its applications across various industries.
Show steps
Show steps
Show steps
- Research and identify real-world applications of Generative AI.
- Write a blog post that clearly explains these applications and their potential impact.
- Share your blog post on social media and engage with readers.
Career center
Learners who complete Introduction to Generative AI will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers create, deploy, and manage machine learning systems. Generative AI is a subfield of machine learning, so this introductory course would provide a strong foundation for a Machine Learning Engineer who wants to learn more about Generative AI.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers develop new AI algorithms and techniques. Generative AI is a rapidly growing field of AI, so an understanding of Generative AI can help AI Researchers stay up-to-date on the latest advances in the field.
Data Scientist
Data Scientist
Data Scientists can create and interpret models generated by Generative AI. This course introduces Generative AI and teaches the theory behind how these models are created. Gaining familiarity with these models would assist a Data Scientist in understanding new interpretations and creations from their models.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems. Generative AI can be used to generate synthetic images, which can be useful for training and testing computer vision systems.
Software Engineer
Software Engineer
Software Engineers design, build, and test software applications. Generative AI can be used in applications such as natural language processing and image generation. Thus, an understanding of Generative AI can be useful for Software Engineers working on these types of applications.
Data Analyst
Data Analyst
Data Analysts clean, analyze, and interpret data to help businesses make informed decisions. Generative AI can be used to generate realistic synthetic data, which can be useful for Data Analysts who need to work with data that is difficult or expensive to obtain.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and implement NLP systems. Generative AI can be used to generate text, which can be useful for training and testing NLP systems.
Game Developer
Game Developer
Game Developers design and develop video games. Generative AI can be used to generate game levels, characters, and other assets.
Animator
Animator
Animators create animations for films, video games, and other applications. Generative AI can be used to generate realistic animations.
Marketer
Marketer
Marketers develop and execute marketing campaigns. Generative AI can be used to generate marketing content.
Product Manager
Product Manager
Product Managers lead the development and launch of new products. Generative AI can be used to generate ideas for new products and features.
Content Creator
Content Creator
Content Creators generate content for websites, blogs, social media, and other platforms. Generative AI can be used to generate text, images, and other types of content.
Filmmaker
Filmmaker
Filmmakers create films and videos. Generative AI can be used to generate realistic backgrounds, characters, and other visual effects.
3D Modeler
3D Modeler
3D Modelers create 3D models for use in video games, movies, and other applications. Generative AI can be used to generate 3D models.
User Experience Designer
User Experience Designer
User Experience Designers create the user interface and user experience for websites and mobile applications. Generative AI can be used to generate realistic images and text, which can be useful for creating prototypes and mockups.
For more career information including salaries, visit:
OpenCourser.com/course/cvp0l1/introduction
Reading list
We've selected 15 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
Introduction to Generative AI.
Provides a comprehensive overview of Generative AI models, from basic concepts to advanced applications. It covers a wide range of topics, including generative adversarial networks (GANs), variational autoencoders (VAEs), and deep generative models.
Provides a comprehensive overview of natural language processing (NLP) with deep learning. It covers a wide range of topics, including language modeling, machine translation, and question answering.
This paper introduces variational autoencoders (VAEs), a type of generative model that has been shown to be very effective for generating images and other complex data.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers a wide range of topics, including image processing, feature detection, and object recognition.
Provides a comprehensive overview of probabilistic graphical models. It covers a wide range of topics, including Bayesian networks, Markov random fields, and Kalman filters.
Provides a comprehensive overview of Bayesian generative models. It covers the theory, implementation, and applications of Bayesian generative models.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, including supervised learning, unsupervised learning, and Bayesian inference.
Provides a comprehensive overview of machine learning for generative AI. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of artificial intelligence for generative AI. It covers the theory, implementation, and applications of artificial intelligence for generative AI.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and statistical modeling.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and statistical modeling.
Provides a comprehensive overview of machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and statistical modeling.
Save
Provides a comprehensive overview of machine learning from an algorithmic perspective. It covers a wide range of topics, including supervised learning, unsupervised learning, and statistical modeling.
Provides a comprehensive overview of machine learning with Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and statistical modeling.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cvp0l1/introduction
Share
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Effort
4 weeks of study, 4 hours / week
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Tom Yeh
Language
English
Transcripts
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Good to know
Introduces generative adversarial networks (GANs) and their use in generating realistic images
Covers language models and their applications in generative AI, such as ChatGPT
Provides a comprehensive overview of generative AI, including its underlying theories and practical considerations
Designed for beginners with little to no prior knowledge of generative AI
Taught by instructors, Tom Yeh, who are recognized for their work in generative AI
Part of a series of courses on generative AI, providing a structured learning path for learners interested in the field
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