Kilian Weinberger is an Associate Professor in the Department of Computer Science at Cornell University. He received his Ph.D. from the University of Pennsylvania in Machine Learning under the supervision of Lawrence Saul and his undergraduate degree in Mathematics and Computer Science from the University of Oxford. During his career he has won several best paper awards at ICML (2004), CVPR (2004, 2017), AISTATS (2005) and KDD (2014, runner-up award). In 2011 he was awarded the Outstanding AAAI Senior Program Chair Award and in 2012 he received an NSF CAREER award. He was elected co-Program Chair for ICML 2016 and for AAAI 2018. In 2016 he was the recipient of the Daniel M Lazar ’29 Excellence in Teaching Award. Kilian Weinberger’s research focuses on Machine Learning and its applications. In particular, he focuses on learning under resource constraints, metric learning, machine learned web-search ranking, computer vision and deep learning. Before joining Cornell University, he was an Associate Professor at Washington University in St. Louis and before that he worked as a research scientist at Yahoo! Research in Santa Clara.
Generative AI and Transformer ModelsCornell Course
Transformer Models
Cornell CourseGenerative AI and Transformer Models ()
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Course Overview
With today's advancements in technology, the realm of generative AI has become more and more prominent. In this course, you will explore the foundation for creating transformer models to generate text and images. You will be guided through each process to generate text using transformers, generate images from images, and generate images from noise. You will be introduced to the building blocks that make up transformers as well as to options for fine-tuning your model to achieve better output results. Through activities and a hands-on project, you'll practice implementing your own generative models and gain the skills and understanding to support your work.
The following courses are required to be completed prior to starting this course:
- Problem-Solving With Machine Learning
- Estimating Probability Distributions
- Learning With Linear Classifiers
- Decision Trees and Model Selection
- Debugging and Improving Machine Learning Models
- Learning With Kernel Machines
- Deep Learning and Neural Networks
Key Course Takeaways
- Identify the functionality of transformers and tasks at each step
- Build a simple transformer language model to generate text
- Determine generative models used to produce an image from another image
- Denoise image data to generate new images
How It Works
Course Length
2 weeks
Effort
6-9 hours per week
Format
100% online, instructor-led
Course Author
Kilian Weinberger
Associate Professor
Cornell Bowers College of Computing and Information Science
Associate Professor, Cornell Computing and Information Science
Who Should Enroll
- Programmers
- Developers
- Data analysts
- Statisticians
- Data scientists
- Software engineers
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