Step confidently into the world of generative AI with our expertly crafted online course, designed to equip you with both foundational knowledge and hands-on experience in cutting-edge deep learning techniques. This course guides you through the essential concepts of how computers interpret and generate images and text, starting with the basics of image representation and progressing through advanced architectures like convolutional neural networks and autoencoders. You’ll explore the power of variational autoencoders and diffusion models, learning how these state-of-the-art tools drive modern image generation and enhancement. With practical exercises using industry-standard libraries such as PyTorch and Hugging Face, you’ll gain direct experience building and deploying generative models for both images and text. The course culminates with an in-depth look at natural language processing pipelines and transformer architectures, empowering you to harness large language models for real-world applications. By the end, you’ll have developed a robust skill set in generative AI, ready to innovate in research, creative industries, or technology-driven businesses. Join us and unlock your potential in the rapidly evolving field of artificial intelligence.

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Programming Generative AI: Unit 2
This course is part of Programming Generative AI Specialization

Instructor: Pearson
Included with
Recommended experience
What you'll learn
Understand and implement core generative AI models for images and text, including autoencoders, diffusion models, and transformers.
Gain practical experience with leading deep learning frameworks such as PyTorch and Hugging Face libraries.
Learn to represent, generate, and manipulate images and text using state-of-the-art neural network architectures.
Apply advanced generative techniques for tasks like image enhancement, translation, and natural language inference.
Skills you'll gain
Details to know

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August 2025
3 assignments
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There is 1 module in this course
This module explores how generative models process and create images and text. Learners will understand image representation, convolutional neural networks, and autoencoders, progressing to variational autoencoders for probabilistic image generation. The module introduces diffusion models and practical image generation using Hugging Face’s diffusers library, including advanced tasks like interpolation and restoration. Shifting to text, it covers natural language processing pipelines, word embeddings, and the transformer architecture, culminating in hands-on experience with large language models using the Hugging Face Transformers library. By the end, students gain both theoretical knowledge and practical skills in multimodal generative AI.
What's included
44 videos3 assignments
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