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Rav Ahuja
This course is for all enthusiasts and practitioners with a genuine interest in the rapidly developing field of generative AI, which is transforming our world.
Read more
This course is for all enthusiasts and practitioners with a genuine interest in the rapidly developing field of generative AI, which is transforming our world.
Read more
This course is for all enthusiasts and practitioners with a genuine interest in the rapidly developing field of generative AI, which is transforming our world.
The course focuses on the core concepts and generative AI models that form the building blocks of generative AI. You will explore deep learning and large language models (LLMs). You will learn about GANs, VAEs, transformers, and diffusion models; the building blocks of generative AI. You will become familiar with the concept of foundation models. You will also learn about the capabilities of pre-trained models and platforms for AI application development and how foundation models use them to generate text, images, and code. You will explore different generative AI platforms like IBM watsonx and Hugging Face.
Hands-on labs, included in the course, provide an opportunity to explore the use cases of generative AI through the IBM generative AI classroom and platforms like IBM watsonx. In this course, you will explore different models, such as IBM Granite, OpenAI GPT, Google flan, and Meta Llama. You will also hear from expert practitioners about the capabilities, applications, and tools of generative AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/vumtzi/generative
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What's inside
Syllabus
Models for Generative AI
In this module, you will dive into the core concepts of generative AI, such as deep learning and LLMs. You will explore the models that form the building blocks of generative AI, including GANs, VAEs, transformers, and diffusion models. You will get acquainted with foundation models and gain insight into how you can use these models as a starting point to generate content.
Read more
Syllabus
Models for Generative AI
In this module, you will dive into the core concepts of generative AI, such as deep learning and LLMs. You will explore the models that form the building blocks of generative AI, including GANs, VAEs, transformers, and diffusion models. You will get acquainted with foundation models and gain insight into how you can use these models as a starting point to generate content.
Read more
Platforms for Generative AI
In this module, you will learn about pre-trained models and platforms for AI application development. You will explore the ability of foundation models to generate text, images, and code using pre-trained models. You will also learn about the features, capabilities, and applications of different platforms, including IBM watsonx and Hugging Face.
Course Quiz, Project, and Wrap-up
This module includes a graded quiz to test and reinforce your understanding of concepts covered in the course. The module also includes a glossary to enhance comprehension of generative AI-related terms.
The module includes a final project, which provides an opportunity to gain hands-on experience on the concepts covered in the course. Finally, the module guides you through the next steps in your learning journey.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides foundational knowledge and skills in generative AI, a rapidly evolving field
Utilizes hands-on labs and different models to enhance practical understanding
Introduces foundational models and their capabilities for generating content
Collaborates with industry experts to provide insights into the applications and capabilities of generative AI
Covers various aspects of generative AI, including deep learning, large language models, and diffusion models
Involves hands-on exploration of use cases through the IBM Generative AI classroom and platforms like IBM Watsonx
Save this course
Save Generative AI: Foundation Models and Platforms 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 Generative AI: Foundation Models and Platforms with these
activities:
Review Probability and Statistics Concepts
Show steps
Review probability and statistics concepts to refresh your knowledge and strengthen your foundation before the course starts.
Browse courses on
Probability
Show steps
-
Go over your notes or textbook
-
Solve practice problems
-
Identify areas where you need further review
Review the Core Concepts of Generative AI
Show steps
Refresh foundational knowledge in deep learning, LLMs, and the different generations of models like GANs and transformers to strengthen understanding of generative AI components.
Browse courses on
Foundation Models
Show steps
-
Revisit concepts of linear algebra and probability.
-
Review the basics of neural networks and deep learning.
-
Explore different generations of generative AI models (GANs, VAEs, Transformers).
-
Examine the architecture and training process of foundation models.
Review Linear Algebra
Show steps
Review the basic concepts of linear algebra, such as vectors, matrices, and linear transformations. This will help you understand the mathematical foundations of generative AI.
Browse courses on
Linear Algebra
Show steps
-
Review your notes or textbook on linear algebra.
-
Solve practice problems on linear algebra.
-
Take a practice quiz or exam on linear algebra.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Gather Resources on Generative AI
Show steps
Organize and expand your learning resources by compiling a list of articles, tutorials, and tools related to generative AI.
Show steps
-
Search for resources online using keywords and reputable sources
-
Create a central repository for all the gathered resources
-
Review and categorize the resources
Seek Mentorship from Generative AI Experts
Show steps
Connect with experts in the field of generative AI through networking events, online platforms, or personal introductions to gain valuable insights.
Show steps
-
Identify potential mentors through professional networks
-
Reach out and introduce yourself
-
Schedule regular meetings to discuss your progress and seek guidance
Explore Hugging Face Tutorial
Show steps
Expand your knowledge of generative AI platforms by following tutorials from Hugging Face.
Browse courses on
Hugging Face
Show steps
-
Complete the Hugging Face tutorial on text generation.
-
Explore additional Hugging Face tutorials on topics of interest.
Follow Tutorials on GPT-3 and Diffusion Models
Show steps
Follow tutorials to explore and apply the capabilities of GPT-3 and diffusion models, deepening your understanding of these models.
Browse courses on
GPT-3
Show steps
-
Find tutorials from reputable sources
-
Complete the tutorials and practice using the models
-
Experiment with different inputs and parameters
Practice Training Generative AI Models with Pre-trained Models
Show steps
Develop practical skills in leveraging pre-trained models to train and fine-tune generative AI models, enhancing understanding of model optimization and deployment.
Browse courses on
Pre-Trained Models
Show steps
-
Choose a suitable pre-trained model for your task.
-
Fine-tune the model on a custom dataset.
-
Evaluate the performance of your model.
-
Deploy your model as an API or in a production environment.
Attend a Workshop on Generative AI Applications
Show steps
Attend a workshop to learn about practical applications of generative AI in natural language processing, computer vision, and other domains.
Browse courses on
Generative AI Applications
Show steps
-
Research and identify relevant workshops
-
Register and attend the workshop
-
Actively participate and take notes
Practice with GANs
Show steps
Develop a deeper understanding of GANs by completing exercises and challenges.
Browse courses on
GANs
Show steps
-
Complete the GANs exercises in the course material.
-
Experiment with different GAN architectures and configurations.
-
Apply GANs to a real-world image generation task.
Build a Generative Art Project
Show steps
Create a generative art project using GANs or VAEs to apply your understanding of these models.
Browse courses on
Generative Art
Show steps
-
Choose a dataset and image style
-
Build a GAN or VAE model
-
Train the model on your dataset
-
Evaluate the results
Create a Text-to-Image Generator
Show steps
Reinforce your understanding of generative AI by building a text-to-image generator using pre-trained models.
Browse courses on
Text-to-Image Generation
Show steps
-
Choose a pre-trained text-to-image model.
-
Build a user interface for your text-to-image generator.
-
Test and deploy your text-to-image generator.
Participate in a Generative AI Hackathon
Show steps
Join a hackathon to test your skills, collaborate with others, and develop innovative generative AI solutions.
Show steps
-
Find relevant hackathons
-
Form a team or join an existing one
-
Brainstorm and develop a project idea
-
Build and present your solution
Review Probability and Statistics Concepts
Show steps
Review probability and statistics concepts to refresh your knowledge and strengthen your foundation before the course starts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Go over your notes or textbook
- Solve practice problems
- Identify areas where you need further review
Review the Core Concepts of Generative AI
Show steps
Refresh foundational knowledge in deep learning, LLMs, and the different generations of models like GANs and transformers to strengthen understanding of generative AI components.
Browse courses on
Foundation Models
Show steps
Show steps
Show steps
- Revisit concepts of linear algebra and probability.
- Review the basics of neural networks and deep learning.
- Explore different generations of generative AI models (GANs, VAEs, Transformers).
- Examine the architecture and training process of foundation models.
Review Linear Algebra
Show steps
Review the basic concepts of linear algebra, such as vectors, matrices, and linear transformations. This will help you understand the mathematical foundations of generative AI.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your notes or textbook on linear algebra.
- Solve practice problems on linear algebra.
- Take a practice quiz or exam on linear algebra.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Gather Resources on Generative AI
Show steps
Organize and expand your learning resources by compiling a list of articles, tutorials, and tools related to generative AI.
Show steps
Show steps
Show steps
- Search for resources online using keywords and reputable sources
- Create a central repository for all the gathered resources
- Review and categorize the resources
Seek Mentorship from Generative AI Experts
Show steps
Connect with experts in the field of generative AI through networking events, online platforms, or personal introductions to gain valuable insights.
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks
- Reach out and introduce yourself
- Schedule regular meetings to discuss your progress and seek guidance
Explore Hugging Face Tutorial
Show steps
Expand your knowledge of generative AI platforms by following tutorials from Hugging Face.
Browse courses on
Hugging Face
Show steps
Show steps
Show steps
- Complete the Hugging Face tutorial on text generation.
- Explore additional Hugging Face tutorials on topics of interest.
Follow Tutorials on GPT-3 and Diffusion Models
Show steps
Follow tutorials to explore and apply the capabilities of GPT-3 and diffusion models, deepening your understanding of these models.
Browse courses on
GPT-3
Show steps
Show steps
Show steps
- Find tutorials from reputable sources
- Complete the tutorials and practice using the models
- Experiment with different inputs and parameters
Practice Training Generative AI Models with Pre-trained Models
Show steps
Develop practical skills in leveraging pre-trained models to train and fine-tune generative AI models, enhancing understanding of model optimization and deployment.
Browse courses on
Pre-Trained Models
Show steps
Show steps
Show steps
- Choose a suitable pre-trained model for your task.
- Fine-tune the model on a custom dataset.
- Evaluate the performance of your model.
- Deploy your model as an API or in a production environment.
Attend a Workshop on Generative AI Applications
Show steps
Attend a workshop to learn about practical applications of generative AI in natural language processing, computer vision, and other domains.
Browse courses on
Generative AI Applications
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Register and attend the workshop
- Actively participate and take notes
Practice with GANs
Show steps
Develop a deeper understanding of GANs by completing exercises and challenges.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Complete the GANs exercises in the course material.
- Experiment with different GAN architectures and configurations.
- Apply GANs to a real-world image generation task.
Build a Generative Art Project
Show steps
Create a generative art project using GANs or VAEs to apply your understanding of these models.
Browse courses on
Generative Art
Show steps
Show steps
Show steps
- Choose a dataset and image style
- Build a GAN or VAE model
- Train the model on your dataset
- Evaluate the results
Create a Text-to-Image Generator
Show steps
Reinforce your understanding of generative AI by building a text-to-image generator using pre-trained models.
Browse courses on
Text-to-Image Generation
Show steps
Show steps
Show steps
- Choose a pre-trained text-to-image model.
- Build a user interface for your text-to-image generator.
- Test and deploy your text-to-image generator.
Participate in a Generative AI Hackathon
Show steps
Join a hackathon to test your skills, collaborate with others, and develop innovative generative AI solutions.
Show steps
Show steps
Show steps
- Find relevant hackathons
- Form a team or join an existing one
- Brainstorm and develop a project idea
- Build and present your solution
Career center
Learners who complete Generative AI: Foundation Models and Platforms will develop knowledge and skills
that may be useful to these careers:
Machine Learning Research Scientist
Machine Learning Research Scientist
Machine Learning Research Scientists research and develop new machine learning algorithms and techniques. They may work in a variety of industries, including technology, academia, and government. This course can help Machine Learning Research Scientists build a foundation for using generative AI techniques in their work. The course covers the core concepts of generative AI, including deep learning and large language models.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers research, develop, and deploy AI systems. They may work in a variety of industries, including technology, finance, and healthcare. This course can help Artificial Intelligence Engineers build a foundation for using generative AI techniques in their work. The course covers the core concepts of generative AI, including deep learning and large language models.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and maintain systems that enable computers to understand human language. They may work in a variety of industries, including technology, finance, and healthcare. This course can help Natural Language Processing Engineers build a foundation for using generative AI techniques in their work. The course covers the core concepts of generative AI, including deep learning and large language models.
Data Science Manager
Data Science Manager
Data Science Managers lead teams of data scientists and engineers to develop and deploy machine learning models. They may work in a variety of industries, including technology, finance, and healthcare. This course can help Data Science Managers build a foundation for using generative AI techniques in their work. The course covers the core concepts of generative AI, including deep learning and large language models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers research, develop, and deploy machine learning models. They may work in a variety of industries, including technology, finance, and healthcare. This course can help Machine Learning Engineers build a foundation for using generative AI techniques in their work. The course covers the core concepts of generative AI, including deep learning and large language models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They help investment firms make informed decisions about which investments to make. This course may be useful for Quantitative Analysts who are interested in using generative AI techniques to improve the accuracy and efficiency of their models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. They may work in a variety of industries, including manufacturing, transportation, and healthcare. This course may be useful for Operations Research Analysts who are interested in using generative AI techniques to improve the efficiency and effectiveness of their work.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by analyzing data and identifying opportunities for improvement. They use a variety of techniques to collect and analyze data, including interviews, surveys, and data mining. This course may be useful for Business Analysts who are interested in using generative AI techniques to improve the accuracy and efficiency of their work.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and maintain systems that enable computers to see and interpret images. They may work in a variety of industries, including technology, manufacturing, and healthcare. This course may be useful for Computer Vision Engineers who are interested in using generative AI techniques to improve the accuracy and efficiency of their systems.
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers develop and maintain systems that enable computers to recognize and understand human speech. They may work in a variety of industries, including technology, healthcare, and customer service. This course may be useful for Speech Recognition Engineers who are interested in using generative AI techniques to improve the accuracy and efficiency of their systems.
Data Scientist
Data Scientist
A Data Scientist uses machine learning algorithms to translate raw data into actionable insights. They build and maintain machine learning models, and may be involved in automating processes or developing custom solutions for an organization's business needs. This course may be useful for Data Scientists who are interested in using generative AI techniques to improve the accuracy and efficiency of their models.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help organizations make informed decisions. They may use a variety of statistical and machine learning techniques to find patterns and trends in data. This course may be useful for Data Analysts who are interested in using generative AI techniques to improve the accuracy and efficiency of their work.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They may specialize in a particular area, such as web development, mobile development, or data science. This course may be useful for Software Engineers who are interested in using generative AI techniques to improve the efficiency and quality of their work.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to create products that meet the needs of customers. This course may be useful for Product Managers who are interested in using generative AI techniques to improve the design and development of their products.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. They may work in a variety of industries, including manufacturing, healthcare, and defense. This course may be useful for Robotics Engineers who are interested in using generative AI techniques to improve the design and development of their robots.
For more career information including salaries, visit:
OpenCourser.com/course/vumtzi/generative
Reading list
We've selected seven 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
Generative AI: Foundation Models and Platforms.
Provides a practical introduction to generative AI, with a focus on using Python to build and train generative models. It valuable resource for anyone looking to get started with generative AI.
Save
Provides a comprehensive overview of the theoretical foundations of generative AI. It valuable resource for anyone looking to gain a deeper understanding of the mathematical and computational principles behind generative models.
Provides a practical guide to using generative AI in business applications. It valuable resource for anyone looking to explore the potential of generative AI for tasks such as data generation, content creation, and product development.
Provides a comprehensive overview of the ethical challenges posed by generative AI. It valuable resource for anyone looking to explore the potential risks and benefits of generative AI, and to develop strategies for ensuring that generative AI is used for good.
Save
Provides a comprehensive overview of the potential risks and benefits of AGI development. It valuable resource for anyone looking to explore the potential implications of generative AI for human society.
Save
Provides a comprehensive overview of the future of humanity in light of the development of generative AI. It valuable resource for anyone looking to explore the potential implications of generative AI for human society.
Provides a comprehensive overview of the potential implications of AGI development for human society. It valuable resource for anyone looking to explore the potential benefits and risks of generative AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vumtzi/generative
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Effort
3 weeks of study, 1–2 hours per week
Level
Introductory
Via
Coursera
Institution
IBM
Instructor
Rav Ahuja
Language
English
Transcripts
Español
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中文
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This course belongs to a
collection
called
Generative AI Fundamentals. It's comprised of three courses:
- Generative AI: Foundation Models and Platforms (viewing)
- Generative AI: Impact, Considerations, and Ethical Issues
- Generative AI: Business Transformation and Career Growth
Good to know
Provides foundational knowledge and skills in generative AI, a rapidly evolving field
Utilizes hands-on labs and different models to enhance practical understanding
Introduces foundational models and their capabilities for generating content
Collaborates with industry experts to provide insights into the applications and capabilities of generative AI
Covers various aspects of generative AI, including deep learning, large language models, and diffusion models
Involves hands-on exploration of use cases through the IBM Generative AI classroom and platforms like IBM Watsonx
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