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Naveed Tauhid
Explore generative AI models and architecture and apply key architectures like VAEs, Transformers, and GANs to real-world problems.
Read more
Explore generative AI models and architecture and apply key architectures like VAEs, Transformers, and GANs to real-world problems.
Read more
Explore generative AI models and architecture and apply key architectures like VAEs, Transformers, and GANs to real-world problems.
Are you fascinated by how AI can create new content, from images to text? Do you want to harness this power and apply it to real-world problems?
In this course, Exploring Generative AI Models and Architecture, you’ll gain the ability to understand, design, and implement generative models.
First, you’ll explore the architecture of Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs), and learn how they generate new data from existing examples.
Next, you’ll discover Transformers and their applications in language translation, text summarization, and chatbots.
Finally, you’ll learn about the architecture of combination models, and how to use them in image captioning, etc. By the end of this course, you'll have gained a greater understanding of generative AI models and architecture.
Register for this course and see more details by visiting:
OpenCourser.com/course/l1q963/exploring
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What's inside
Syllabus
Course Overview
Introduction to Generative Models
Variational Autoencoders (VAEs)
Transformers
Read more
Syllabus
Course Overview
Introduction to Generative Models
Variational Autoencoders (VAEs)
Transformers
Read more
Chatbots
Generative Adversarial Networks (GANs)
Combination Models
Large Language Models (LLMs)
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a comprehensive introduction to generative AI models and architectures
Suitable for learners with a background in machine learning and deep learning
Covers a range of topics, including VAEs, Transformers, GANs, and combination models
Practical applications of generative AI are emphasized in real-world case studies
Taught by experienced instructors who are active in the field of generative AI research
Save this course
Save Exploring Generative AI Models and Architecture 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 Exploring Generative AI Models and Architecture with these
activities:
Read 'Generative Deep Learning' by David Foster
Show steps
Gain a theoretical foundation in generative deep learning models, including VAEs, GANs, and Transformers.
View
Generative Deep Learning: Teaching Machines To...
on Amazon
Show steps
-
Read the book's chapters on VAE architectures.
-
Read the book's chapters on GAN architectures.
-
Read the book's chapters on Transformer architectures.
Follow Tutorials on Building Generative AI Models
Show steps
Enhance your practical skills by following online tutorials on building and deploying generative AI models.
Browse courses on
Generative AI
Show steps
-
Identify reputable sources for tutorials.
-
Select tutorials that align with your learning goals.
-
Follow the tutorials step-by-step.
-
Experiment with different ideas and techniques.
-
Share your learnings and ask questions in the tutorial forums.
Join a Study Group on Generative AI
Show steps
Engage in collaborative learning and discussions with peers to deepen your understanding of generative AI concepts.
Browse courses on
Generative AI
Show steps
-
Identify or create a study group with like-minded individuals.
-
Establish regular meeting times and topics for discussion.
-
Share resources, ideas, and experiences related to generative AI.
-
Provide constructive feedback and support to group members.
-
Reflect on your learning and identify areas for improvement.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a Variational Autoencoder in Python
Show steps
Develop a practical understanding of VAE architectures and their implementation in Python.
Browse courses on
Variational Autoencoders
Show steps
-
Set up a Python development environment.
-
Import the necessary libraries (e.g., TensorFlow, Keras).
-
Design and implement a simple VAE model.
-
Train the VAE model on a dataset.
-
Evaluate the VAE model's performance.
Practice Generative Adversarial Network (GAN) Architectures
Show steps
Strengthen your understanding of GANs by solving exercises and implementing different architectures.
Browse courses on
Generative Adversarial Networks
Show steps
-
Implement a basic GAN model from scratch.
-
Train the GAN model on a dataset of images.
-
Evaluate the GAN model's performance.
-
Experiment with different GAN architectures (e.g., DCGAN, WGAN).
-
Apply GANs to generate images or other types of data.
Create a Chatbot with a Transformer Architecture
Show steps
Gain hands-on experience building and deploying a chatbot using Transformer models.
Browse courses on
Transformers
Show steps
-
Choose a pre-trained Transformer model.
-
Design and implement a chatbot interface.
-
Train the chatbot on a dialogue dataset.
-
Deploy the chatbot on a platform.
-
Evaluate the chatbot's performance and user experience.
Develop a Generative AI Proof-of-Concept
Show steps
Apply your knowledge by creating a working prototype of a generative AI solution.
Browse courses on
Generative AI
Show steps
-
Identify a problem or opportunity that can be addressed with generative AI.
-
Research and select suitable generative AI models.
-
Design and implement the proof-of-concept.
-
Evaluate the proof-of-concept's performance and feasibility.
-
Document your findings and present them to stakeholders.
Read 'Generative Deep Learning' by David Foster
Show steps
Gain a theoretical foundation in generative deep learning models, including VAEs, GANs, and Transformers.
View
Generative Deep Learning: Teaching Machines To...
on Amazon
Show steps
Show steps
Show steps
- Read the book's chapters on VAE architectures.
- Read the book's chapters on GAN architectures.
- Read the book's chapters on Transformer architectures.
Follow Tutorials on Building Generative AI Models
Show steps
Enhance your practical skills by following online tutorials on building and deploying generative AI models.
Browse courses on
Generative AI
Show steps
Show steps
Show steps
- Identify reputable sources for tutorials.
- Select tutorials that align with your learning goals.
- Follow the tutorials step-by-step.
- Experiment with different ideas and techniques.
- Share your learnings and ask questions in the tutorial forums.
Join a Study Group on Generative AI
Show steps
Engage in collaborative learning and discussions with peers to deepen your understanding of generative AI concepts.
Browse courses on
Generative AI
Show steps
Show steps
Show steps
- Identify or create a study group with like-minded individuals.
- Establish regular meeting times and topics for discussion.
- Share resources, ideas, and experiences related to generative AI.
- Provide constructive feedback and support to group members.
- Reflect on your learning and identify areas for improvement.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a Variational Autoencoder in Python
Show steps
Develop a practical understanding of VAE architectures and their implementation in Python.
Browse courses on
Variational Autoencoders
Show steps
Show steps
Show steps
- Set up a Python development environment.
- Import the necessary libraries (e.g., TensorFlow, Keras).
- Design and implement a simple VAE model.
- Train the VAE model on a dataset.
- Evaluate the VAE model's performance.
Practice Generative Adversarial Network (GAN) Architectures
Show steps
Strengthen your understanding of GANs by solving exercises and implementing different architectures.
Browse courses on
Generative Adversarial Networks
Show steps
Show steps
Show steps
- Implement a basic GAN model from scratch.
- Train the GAN model on a dataset of images.
- Evaluate the GAN model's performance.
- Experiment with different GAN architectures (e.g., DCGAN, WGAN).
- Apply GANs to generate images or other types of data.
Create a Chatbot with a Transformer Architecture
Show steps
Gain hands-on experience building and deploying a chatbot using Transformer models.
Browse courses on
Transformers
Show steps
Show steps
Show steps
- Choose a pre-trained Transformer model.
- Design and implement a chatbot interface.
- Train the chatbot on a dialogue dataset.
- Deploy the chatbot on a platform.
- Evaluate the chatbot's performance and user experience.
Develop a Generative AI Proof-of-Concept
Show steps
Apply your knowledge by creating a working prototype of a generative AI solution.
Browse courses on
Generative AI
Show steps
Show steps
Show steps
- Identify a problem or opportunity that can be addressed with generative AI.
- Research and select suitable generative AI models.
- Design and implement the proof-of-concept.
- Evaluate the proof-of-concept's performance and feasibility.
- Document your findings and present them to stakeholders.
Career center
Learners who complete Exploring Generative AI Models and Architecture will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models to solve complex problems. They use their knowledge of mathematics, statistics, and computer science to develop algorithms that can learn from data and make predictions. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new machine learning models.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to develop models that can predict future outcomes or make recommendations. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new data science models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of computer science and programming to create software that meets the needs of users. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new software applications.
Data Architect
Data Architect
Data Architects design and manage data systems. They use their knowledge of data management and architecture to create systems that can store, process, and analyze data efficiently. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new data architectures.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They use their knowledge of database management systems to ensure that databases are running smoothly and efficiently. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new database management systems.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. They use their knowledge of finance and economics to analyze financial statements and make recommendations about which investments to buy or sell. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new financial models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use their knowledge of finance and mathematics to develop models that can predict future market trends. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new quantitative models.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They use their knowledge of mathematics, statistics, and finance to develop models that can predict the likelihood of future events and calculate the financial impact of those events. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new actuarial models.
Market Research Analyst
Market Research Analyst
Market Research Analysts use data to understand consumer behavior and make marketing decisions. They use their knowledge of marketing and statistics to develop surveys, conduct interviews, and analyze data to identify trends and patterns. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new market research models.
Systems Analyst
Systems Analyst
Systems Analysts design and implement computer systems. They use their knowledge of computer science and systems analysis to create systems that meet the needs of users. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new systems.
Computer Programmer
Computer Programmer
Computer Programmers write code to implement computer systems. They use their knowledge of programming languages and software development to create programs that meet the needs of users. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new computer programs.
Software Tester
Software Tester
Software Testers test software to ensure that it is working correctly. They use their knowledge of software testing and quality assurance to find and fix bugs. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new software testing tools.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They use their knowledge of business and data analysis to identify opportunities for improvement and develop solutions. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new business models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve problems in business and industry. They use their knowledge of mathematics, statistics, and operations research to develop models that can improve efficiency, reduce costs, and make better decisions. This course would provide a strong foundation in the principles of generative AI, which are essential for developing new operations research models.
Technical Writer
Technical Writer
Technical Writers write documentation for computer systems and software. They use their knowledge of technical writing and documentation to create documents that are clear, concise, and easy to understand. This course may be helpful for Technical Writers who want to learn more about the principles of generative AI and how they can be used to improve the quality of technical documentation.
For more career information including salaries, visit:
OpenCourser.com/course/l1q963/exploring
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
Exploring Generative AI Models and Architecture.
Save
Is the definitive guide to generative adversarial networks (GANs). It provides a detailed overview of the theory and practice of GANs, including their architecture, training algorithms, and applications. It must-read for anyone interested in learning about GANs.
Provides a comprehensive overview of deep learning with Python. It covers topics such as image classification, object detection, and natural language processing. It also provides guidance on building and deploying deep learning models with Keras.
This paper introduces variational autoencoders (VAEs), which are a type of generative model that can be used to learn the underlying distribution of data. VAEs are particularly useful for generating new data that is similar to the training data.
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers topics such as text classification, sentiment analysis, machine translation, and question answering. It also provides practical guidance on building and deploying deep learning models for NLP tasks.
Provides a practical introduction to deep learning for coders. It covers topics such as image classification, object detection, and natural language processing. It also provides guidance on building and deploying deep learning models with Fastai and PyTorch.
Provides a practical introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers topics such as supervised learning, unsupervised learning, and deep learning. It also provides guidance on building and deploying machine learning models.
Provides a concise overview of machine learning. It covers topics such as supervised learning, unsupervised learning, and deep learning. It also provides guidance on building and deploying machine learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l1q963/exploring
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Time
48 hours
Level
Intermediate
Via
Pluralsight
Instructor
Naveed Tauhid
Language
English
Good to know
Provides a comprehensive introduction to generative AI models and architectures
Suitable for learners with a background in machine learning and deep learning
Covers a range of topics, including VAEs, Transformers, GANs, and combination models
Practical applications of generative AI are emphasized in real-world case studies
Taught by experienced instructors who are active in the field of generative AI research
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