Packt

Transformers for Vision AI, Multimodal & Generative AI

Packt

Transformers for Vision AI, Multimodal & Generative AI

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand and compare major transformer models like BERT, GPT, and ViT

  • Fine-tune and pretrain large language models for specific tasks

  • Implement retrieval augmented generation to improve model reliability

Details to know

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Recently updated!

July 2026

Assessments

6 assignments

Taught in English

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This course is part of the Transformers for NLP and Computer Vision Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

This module introduces learners to vision transformers and multimodal AI models, including ViT, CLIP, and DALL-E. You will explore how images are processed as input for transformers, understand the architecture and configuration of feature extractors, and examine real-world applications of these models in creative and mainstream contexts.

What's included

1 video7 readings1 assignment

This module introduces the fundamentals of Stable Diffusion for generating images and videos from text prompts. Learners will explore the underlying architecture, practical implementations using Keras and Hugging Face, and the adaptation of OpenAI CLIP for text-to-video synthesis. By the end, participants will gain hands-on experience running diffusion models and understanding their creative potential.

What's included

1 video3 readings1 assignment

This module guides learners through the process of training and deploying vision models using Hugging Face AutoTrain, all without writing code. You will explore data preparation, model selection, and evaluation techniques, while gaining hands-on experience with popular architectures like ViT, BEiT, and ConvNext.

What's included

1 video5 readings1 assignment

This module introduces learners to the concept of Functional Artificial General Intelligence (F-AGI) and demonstrates how advanced AI platforms like HuggingGPT and Google Cloud Vision can be integrated for complex task automation. Learners will explore model chaining, cross-platform AI pipelines, and practical approaches to enhancing computer vision accuracy in challenging scenarios.

What's included

1 video6 readings1 assignment

This module introduces automated generative ideation systems, demonstrating how AI tools like ChatGPT, Llama 2, Midjourney, and Microsoft Designer can streamline content and image creation without manual prompts. Learners will explore practical workflows, ethical considerations, and integration strategies for efficient, scalable ideation. By the end, you'll be equipped to implement and extend automated pipelines for creative tasks.

What's included

1 video6 readings2 assignments

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