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Pearson
Programming Generative AI Specialization

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Pearson

Programming Generative AI Specialization

Deep Generative AI Neural Network Multimodal Model. A journey through deep generative modeling鈥攂uild, train, and personalize AI models using PyTorch.

Pearson

Instructor: Pearson

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 weeks to complete
at 5 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 weeks to complete
at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Develop and implement deep generative models for images and text using PyTorch and Hugging Face libraries.

  • Build and personalize state-of-the-art multimodal systems, including text-to-image generators like stable diffusion.

  • Apply advanced techniques for data manipulation, model evaluation, and efficient fine-tuning of large pre-trained AI models.

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Taught in English
Recently updated!

September 2025

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Specialization - 3 course series

What you'll learn

  • Develop a foundational understanding of generative AI and deep generative modeling concepts.

  • Explore and compare various multimodal generative models and their input/output modalities.

  • Gain hands-on experience programming with tensors and building neural networks using PyTorch.

  • Understand the theoretical trade-offs between different generative model architectures and their practical implications.

Skills you'll gain

Category: Generative Model Architectures
Category: PyTorch (Machine Learning Library)
Category: Generative AI
Category: Data Manipulation
Category: Artificial Neural Networks
Category: Deep Learning
Category: Multimodal Prompts

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

Category: Generative Model Architectures
Category: Natural Language Processing
Category: Large Language Modeling
Category: Image Analysis
Category: Text Mining
Category: Generative AI
Category: Unsupervised Learning
Category: PyTorch (Machine Learning Library)
Category: Artificial Neural Networks
Category: Computer Vision
Category: Deep Learning

What you'll learn

  • Understand and implement multimodal models that integrate images and text for advanced AI applications.

  • Build and optimize semantic image search engines using contrastive language-image pre-training.

  • Master the principles and practicalities of latent diffusion and stable diffusion for text-to-image generation.

  • Adapt, fine-tune, and efficiently evaluate pre-trained generative models for new tasks, styles, and real-time performance.

Skills you'll gain

Category: Generative Model Architectures
Category: Generative AI
Category: Performance Tuning
Category: Image Quality
Category: Image Analysis
Category: Computer Vision
Category: Prompt Engineering
Category: Multimodal Prompts
Category: Natural Language Processing
Category: Deep Learning

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Instructor

Pearson
Pearson
234 Courses5,372 learners

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Pearson

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