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⇱ Programming Generative AI: Unit 2 | Coursera


Programming Generative AI: Unit 2

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Programming Generative AI: Unit 2

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

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace

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

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace

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.

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Assessments

3 assignments

Taught in English

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This course is part of the Programming Generative AI 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 is 1 module in this course

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.

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

44 videosβ€’Total 416 minutes
  • Topicsβ€’1 minute
  • Representing Images as Tensorsβ€’8 minutes
  • Desiderata for Computer Visionβ€’5 minutes
  • Features of Convolutional Neural Networksβ€’8 minutes
  • Working with Images in Pythonβ€’10 minutes
  • The FashionMNIST Datasetβ€’5 minutes
  • Convolutional Neural Networks in PyTorchβ€’11 minutes
  • Components of a Latent Variable Model (LVM)β€’9 minutes
  • The Humble Autoencoderβ€’5 minutes
  • Defining an Autoencoder with PyTorchβ€’6 minutes
  • Setting up a Training Loopβ€’10 minutes
  • Inference with an Autoencoderβ€’4 minutes
  • Look Ma, No Features!β€’8 minutes
  • Adding Probability to Autoencoders (VAE)β€’5 minutes
  • Variational Inference: Not Just for Autoencodersβ€’7 minutes
  • Transforming an Autoencoder into a VAEβ€’13 minutes
  • Training a VAE with PyTorchβ€’14 minutes
  • Exploring Latent Spaceβ€’12 minutes
  • Latent Space Interpolation and Attribute Vectorsβ€’13 minutes
  • Topicsβ€’1 minute
  • Generation as a Reversible Processβ€’5 minutes
  • Sampling as Iterative Denoisingβ€’4 minutes
  • Diffusers and the Hugging Face Ecosystemβ€’7 minutes
  • Generating Images with Diffusers Pipelinesβ€’28 minutes
  • Deconstructing the Diffusion Processβ€’19 minutes
  • Forward Process as Encoderβ€’17 minutes
  • Reverse Process as Decoderβ€’7 minutes
  • Interpolating Diffusion Modelsβ€’9 minutes
  • Image-to-Image Translation with SDEditβ€’8 minutes
  • Image Restoration and Enhancementβ€’11 minutes
  • Topicsβ€’1 minute
  • The Natural Language Processing Pipelineβ€’13 minutes
  • Generative Models of Languageβ€’10 minutes
  • Generating Text with Transformers Pipelinesβ€’15 minutes
  • Deconstructing Transformers Pipelinesβ€’8 minutes
  • Decoding Strategiesβ€’13 minutes
  • Transformers are Just Latent Variable Models for Sequencesβ€’12 minutes
  • Visualizing and Understanding Attentionβ€’24 minutes
  • Turning Words into Vectorsβ€’11 minutes
  • The Vector Space Modelβ€’7 minutes
  • Embedding Sequences with Transformersβ€’10 minutes
  • Computing the Similarity Between Embeddingsβ€’8 minutes
  • Semantic Search with Embeddingsβ€’7 minutes
  • Contrastive Embeddings with Sentence Transformersβ€’7 minutes
3 assignmentsβ€’Total 90 minutes
  • Latent Space Rules Everything Around Me Quizβ€’30 minutes
  • Demystifying Diffusion Quizβ€’30 minutes
  • Generating and Encoding Text with Transformers Quizβ€’30 minutes

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Instructors

Pearson
268 Coursesβ€’65,144 learners

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