Generative AI and LLMs: Architecture and Data Preparation
Master generative AI fundamentals and learn to prepare data for Large Language Models with hands-on PyTorch implementation.
Master generative AI fundamentals and learn to prepare data for Large Language Models with hands-on PyTorch implementation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Generative AI Engineering with LLMs Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Understand different generative AI architectures and models
Master tokenization methods for text data preparation
Implement NLP data loaders using PyTorch
Work with popular LLMs like GPT and BERT
Use Hugging Face libraries for model implementation
Create custom tokenizers for text processing
Skills you'll gain
This course includes:
0.6 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 2 modules in this course
This comprehensive course explores the fundamentals of generative AI and Large Language Models (LLMs). Students learn about various generative AI architectures including RNNs, Transformers, VAEs, GANs, and Diffusion Models. The curriculum covers essential concepts in tokenization, data preparation, and model implementation using PyTorch and Hugging Face libraries. Through hands-on labs, learners gain practical experience in implementing tokenization methods and creating NLP data loaders for training generative AI models.
Generative AI Architecture
Module 1 · 2 Hours to complete
Data Preparation for LLMs
Module 2 · 2 Hours to complete
Fee Structure
Instructors
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
Emerging Talent in Generative AI at IBM
Roodra Pratap Kanwar is a Data Scientist Intern at IBM, where he is actively involved in developing skills in generative AI and large language models (LLMs). Currently enrolled in a professional certificate program, Roodra focuses on the architecture and data preparation necessary for effective AI applications. With a strong foundation in machine learning and data science, he is dedicated to bridging theoretical concepts with practical implementations, aiming to enhance the accessibility and usability of AI technologies. Roodra has also taken on the role of instructor for an online course on Coursera, where he shares his knowledge of generative AI with a global audience. His commitment to advancing his expertise in this rapidly evolving field positions him as a promising contributor to the future of AI and data science.
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4.3 course rating
12 ratings
Frequently asked questions
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