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Rav Ahuja
This course is designed for enthusiasts and practitioners who share an interest in the rapidly advancing field of generative AI.
This course centers around the core concepts and generative AI models that form the building blocks of generative AI. You will delve into the concepts of deep learning and large language models (LLMs). You will learn about GANs, VAEs, transformers, and diffusion models – the fundamental components of generative AI.
Read more
This course is designed for enthusiasts and practitioners who share an interest in the rapidly advancing field of generative AI.
This course centers around the core concepts and generative AI models that form the building blocks of generative AI. You will delve into the concepts of deep learning and large language models (LLMs). You will learn about GANs, VAEs, transformers, and diffusion models – the fundamental components of generative AI.
Read more
This course is designed for enthusiasts and practitioners who share an interest in the rapidly advancing field of generative AI.
This course centers around the core concepts and generative AI models that form the building blocks of generative AI. You will delve into the concepts of deep learning and large language models (LLMs). You will learn about GANs, VAEs, transformers, and diffusion models – the fundamental components of generative AI.
You will learn about the concept of foundation models. You will gain insights into the capabilities of pre-trained models and platforms for AI application development. The course will also cover how foundation models utilize these platforms to generate text, images, and code. Additionally, participants will explore various generative AI platforms such as IBM watsonX and Hugging Face.
The course includes practical hands-on labs, offering participants the chance to delve into the applications of generative AI using the IBM Generative AI Classroom and platforms like IBM watsonX. Throughout the course, you'll have the opportunity to explore various models, including IBM Granite, OpenAI GPT, Google Flan, and Meta Llama. Additionally, expert practitioners will share insights into the capabilities, applications, and tools of generative AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/k3rugq/models
What's inside
Learning objectives
- Describe the fundamental concepts of generative ai.
- Explore the building blocks of generative ai, including gans, vaes, transformers, and diffusion models.
- Explain the concept of foundation models in generative ai.
- Explore the ability of foundation models to generate text, images, and code using pre-trained models.
- Describe the features, capabilities, and applications of different generative ai platforms, including ibm watsonx and hugging face.
- Describe the fundamental concepts of generative ai.
- Explore the building blocks of generative ai, including gans, vaes, transformers, and diffusion models.
- Explain the concept of foundation models in generative ai.
- Explore the ability of foundation models to generate text, images, and code using pre-trained models.
- Describe the features, capabilities, and applications of different generative ai platforms, including ibm watsonx and hugging face.
Syllabus
Module 1: Models for Generative AI
Video: Course Introduction
Reading: Course Overview
Reading: Program Overview
Read more
Syllabus
Module 1: Models for Generative AI
Video: Course Introduction
Reading: Course Overview
Reading: Program Overview
Read more
Reading: Helpful Tips for Course Completion
Video: Deep Learning and Large Language Models
Video: Generative AI Models
Video: Foundation Models
Hands-on Labs: Generative AI Foundation Models
Reading: Module Summary
Practice Quiz: Core Concepts and Models of Generative AI
Discussion Prompt: Working with Foundation Models
Reading: IBM Granite Foundation Models
Graded Quiz: Models for Generative AI
Module 2: Platforms for Generative AI
Video: Pre-trained Models: Text-to-Text Generation
Hands-on Lab: Develop AI Applications with the Foundation Models
Video: Pre-trained Models: Text-to-Image Generation
Video: Pre-trained Models: Text-to-Code Generation
Hands-on Lab: Develop AI Applications for Code Generation
Video: IBM watsonx.ai
Video 5: Hugging Face
Practice Quiz: Pre-trained Models and Platforms for AI Applications Development
Graded Quiz: Platforms for Generative AI
Module 3: Course Quiz, Project, and Wrap-up
Glossary - Generative AI: Foundation Models and Platforms
Final Project: Working with IBM Granite Foundation Models
Graded Quiz: Generative AI: Foundation Models and Platforms
Reading: Congratulations and Next Steps
Reading: Thanks from the Course Team
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by RAV Ahuja, an expert in the field of generative AI and deep learning
Suitable for those with a background in deep learning and machine learning
Covers advanced topics in generative AI, including foundation models and pre-trained models
Provides hands-on labs and access to platforms like IBM WatsonX and Hugging Face for practical experience
Shares insights from expert practitioners in the field
Students can explore various generative AI models, including IBM Granite, OpenAI GPT, Google Flan, and Meta Llama
Save this course
Save Models and Platforms for Generative AI to your list so you can find it easily later:
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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 Models and Platforms for Generative AI with these
activities:
Review Deep Learning, Natural Language Processing, and Machine Learning
Show steps
Revisiting knowledge of these topics may help prepare you for the core learning objectives.
Browse courses on
Deep Learning
Show steps
-
Review and summarize key concepts from deep learning
-
Familiarize yourself with natural language processing (NLP) techniques
-
Brush up on machine learning algorithms
Explore Generative AI Toolkits and Platforms
Show steps
Expand your knowledge by exploring different generative AI toolkits and platforms.
Show steps
-
Research and identify different generative AI toolkits and platforms
-
Explore the documentation and tutorials of these platforms
Join Study Group or Peer Discussion
Show steps
Connect with other students and challenge your understanding of course concepts through discussions.
Show steps
-
Identify or create a study group or discussion forum
-
Participate actively in group discussions and knowledge sharing
Three other activities
Expand to see all activities and additional details
Show all six activities
Complete Hands-on Labs and Practice Exercises
Show steps
Reinforce your understanding of the concepts by working on the course's hands-on exercises and labs.
Browse courses on
Hands-on Labs
Show steps
-
Work through the hands-on labs in the course
-
Complete the practice exercises provided in the course material
Compile Resources on Generative AI
Show steps
Organize and share resources to enhance your understanding of generative AI.
Show steps
-
Gather high-quality articles, tutorials, and code samples on generative AI
-
Organize these resources into a coherent collection
-
Share your compilation with other students or the wider community
Build a Generative AI Model
Show steps
Apply your knowledge by building a generative AI model of your own.
Browse courses on
Generative AI Models
Show steps
-
Choose a generative AI model type (e.g., GAN, VAE, transformer, diffusion model)
-
Gather and prepare your data
-
Implement the model architecture
-
Train and fine-tune your model
-
Evaluate the performance of your model
-
Document your work and findings in a report
Review Deep Learning, Natural Language Processing, and Machine Learning
Show steps
Revisiting knowledge of these topics may help prepare you for the core learning objectives.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Review and summarize key concepts from deep learning
- Familiarize yourself with natural language processing (NLP) techniques
- Brush up on machine learning algorithms
Explore Generative AI Toolkits and Platforms
Show steps
Expand your knowledge by exploring different generative AI toolkits and platforms.
Show steps
Show steps
Show steps
- Research and identify different generative AI toolkits and platforms
- Explore the documentation and tutorials of these platforms
Join Study Group or Peer Discussion
Show steps
Connect with other students and challenge your understanding of course concepts through discussions.
Show steps
Show steps
Show steps
- Identify or create a study group or discussion forum
- Participate actively in group discussions and knowledge sharing
Three other activities
Expand to see all activities and additional details
Show all six activities
Complete Hands-on Labs and Practice Exercises
Show steps
Reinforce your understanding of the concepts by working on the course's hands-on exercises and labs.
Browse courses on
Hands-on Labs
Show steps
Show steps
Show steps
- Work through the hands-on labs in the course
- Complete the practice exercises provided in the course material
Compile Resources on Generative AI
Show steps
Organize and share resources to enhance your understanding of generative AI.
Show steps
Show steps
Show steps
- Gather high-quality articles, tutorials, and code samples on generative AI
- Organize these resources into a coherent collection
- Share your compilation with other students or the wider community
Build a Generative AI Model
Show steps
Apply your knowledge by building a generative AI model of your own.
Browse courses on
Generative AI Models
Show steps
Show steps
Show steps
- Choose a generative AI model type (e.g., GAN, VAE, transformer, diffusion model)
- Gather and prepare your data
- Implement the model architecture
- Train and fine-tune your model
- Evaluate the performance of your model
- Document your work and findings in a report
Career center
Learners who complete Models and Platforms for Generative AI will develop knowledge and skills
that may be useful to these careers:
AI Researcher
AI Researcher
AI Researchers explore new and emerging AI technologies and applications. This course provides AI Researchers with the knowledge and skills necessary to understand and apply generative AI models to real-world problems, contributing to the advancement of AI research and development.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and deploy AI models that can understand and generate human language. This course provides the necessary knowledge and skills to apply generative AI models to NLP tasks, such as text generation, language translation, and sentiment analysis.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and deploy AI models that can analyze and interpret images and videos. This course provides a strong foundation in generative AI models that can be used to create computer vision applications for object detection, image segmentation, and facial recognition.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models to solve real-world problems. This course can help Machine Learning Engineers build a foundation in generative AI models and gain experience in using them to develop innovative AI applications.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting large and complex data sets. This course would be useful for Data Scientists as it provides the foundation and hands-on experience necessary to understand and apply generative AI models to data analysis and decision-making.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides Software Engineers with the foundation and practical experience necessary to incorporate generative AI models into their software applications, enhancing their functionality and user experience.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to provide insights and recommendations. This course may be useful for Data Analysts who want to stay updated on the latest trends in generative AI and learn how to apply these models to improve their data analysis and decision-making.
Business Analyst
Business Analyst
Business Analysts analyze business processes and recommend solutions to improve efficiency and effectiveness. This course may be helpful for Business Analysts who want to gain a foundational understanding of generative AI and how it can be used to improve business outcomes.
Product Manager
Product Manager
Product Managers define and manage the development and launch of new products. This course may be useful for Product Managers who want to learn about the potential applications of generative AI and how it can be used to enhance product development and innovation.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. This course may be useful for Marketing Managers who want to learn about the use of generative AI in marketing to create personalized content, automate marketing tasks, and optimize marketing campaigns.
UX Designer
UX Designer
UX Designers design and enhance the user experience of products and services. This course may be useful for UX Designers who want to explore the potential of generative AI to improve the user experience through personalized interactions, content generation, and user interface design.
Content Writer
Content Writer
Content Writers create and edit written content for various purposes. This course may be useful for Content Writers who want to learn about the use of generative AI in content creation to generate ideas, assist with writing, and improve content quality and engagement.
Technical Writer
Technical Writer
Technical Writers create and edit technical documentation for software, hardware, and other technical products. This course may be useful for Technical Writers who want to learn about the use of generative AI to automate documentation generation, improve documentation quality, and enhance the user experience.
Instructional Designer
Instructional Designer
Instructional Designers design and develop educational materials and learning experiences. This course may be useful for Instructional Designers who want to explore the use of generative AI to create personalized learning experiences, develop interactive simulations, and enhance the overall effectiveness of training and education.
Customer Success Manager
Customer Success Manager
Customer Success Managers build and maintain relationships with customers to ensure their satisfaction and success. This course may be useful for Customer Success Managers who want to learn about the use of generative AI to improve customer engagement, provide personalized support, and enhance the customer experience.
For more career information including salaries, visit:
OpenCourser.com/course/k3rugq/models
Reading list
We've selected six 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
Models and Platforms for Generative AI.
Provides a rigorous and comprehensive treatment of the foundations of machine learning. This book valuable reference for those who want to develop a deep understanding of the theoretical underpinnings of generative AI.
Provides a gentle introduction to deep learning and is helpful to those who do not have a background in machine learning or statistics. This book is widely used as a textbook for deep learning at academic institutions.
Provides a practical guide to deep learning for coders, using the Fastai and PyTorch libraries. This book helpful resource for those who want to use generative AI for practical applications.
Provides a comprehensive overview of reinforcement learning, a type of machine learning that is well-suited for solving problems that require decision-making. This book valuable reference for those who want to develop a deeper understanding of the technical aspects of generative AI.
Provides a concise overview of machine learning. The book can be used as a refresher for those who are already familiar with machine learning, and for those with limited exposure to machine learning, the book can be used to quickly build up core concepts.
Explores the potential risks and challenges of generative AI, and how to align AI with human values. This book provides an ethical perspective on the development and use of generative AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k3rugq/models
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Effort
1 to 3 hours per week for 3 weeks
Level
Introductory
Via
edX
Institution
IBM
Instructor
Rav Ahuja
Session
February 26, 2024
- January 31, 2025
Language
English
Transcript
English
Good to know
Taught by RAV Ahuja, an expert in the field of generative AI and deep learning
Suitable for those with a background in deep learning and machine learning
Covers advanced topics in generative AI, including foundation models and pre-trained models
Provides hands-on labs and access to platforms like IBM WatsonX and Hugging Face for practical experience
Shares insights from expert practitioners in the field
Students can explore various generative AI models, including IBM Granite, OpenAI GPT, Google Flan, and Meta Llama
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