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⇱ Programming Generative AI: Unit 1 | Coursera


Programming Generative AI: Unit 1

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Programming Generative AI: Unit 1

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

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

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

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

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.

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Assessments

2 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

Unlock the transformative power of generative AI with our comprehensive online course, designed for learners eager to master the fundamentals and practical applications of deep generative modeling. Begin your journey by demystifying what generative AI truly is, exploring the diverse landscape of multimodal models, and understanding how algorithms can create rich media content from scratch. Delve into the theoretical underpinnings and formalizations that drive deep generative models, gaining insight into the trade-offs between different architectures. Transition seamlessly from theory to practice as you are introduced to the PyTorch frameworkβ€”one of the most powerful tools in modern deep learning. Through hands-on programming exercises, you’ll learn to manipulate tensors, leverage automatic differentiation, and harness GPU acceleration to build and train your own neural networks. By the end of this course, you’ll not only grasp the core concepts behind generative AI but also acquire the practical skills needed to implement and experiment with deep learning models using industry-standard tools. Whether you’re aspiring to innovate in AI research or apply these skills in real-world projects, this course is your gateway to the future of artificial intelligence.

This module introduces the fundamentals of generative AI and deep generative modeling, exploring how algorithms can create rich media across various modalities. It covers the theoretical foundations and trade-offs of different generative model architectures. The module then provides hands-on experience with the PyTorch framework, guiding learners through programming with tensors, leveraging automatic differentiation, and building neural networks. By the end, students will understand both the principles behind generative models and the practical skills needed to implement them using modern deep learning tools.

What's included

29 videos2 assignments

29 videosβ€’Total 272 minutes
  • Specialization Introductionβ€’5 minutes
  • Topicsβ€’1 minute
  • Generative AI in the Wildβ€’10 minutes
  • Defining Generative AIβ€’6 minutes
  • Multitudes of Mediaβ€’13 minutes
  • How Machines Createβ€’12 minutes
  • Formalizing Generative Modelsβ€’13 minutes
  • Generative versus Discriminative Modelsβ€’11 minutes
  • The Generative Modeling Trilemmaβ€’7 minutes
  • Introduction to Google Colabβ€’20 minutes
  • Topicsβ€’1 minute
  • What Is PyTorch?β€’5 minutes
  • The PyTorch Layer Cakeβ€’11 minutes
  • The Deep Learning Software Trilemmaβ€’7 minutes
  • What Are Tensors, Really?β€’5 minutes
  • Tensors in PyTorchβ€’10 minutes
  • Introduction to Computational Graphsβ€’13 minutes
  • Backpropagation Is Just the Chain Ruleβ€’17 minutes
  • Effortless Backpropagation with torch.autogradβ€’14 minutes
  • PyTorch's Device Abstraction (i.e., GPUs)β€’4 minutes
  • Working with Devicesβ€’11 minutes
  • Components of a Learning Algorithmβ€’7 minutes
  • Introduction to Gradient Descentβ€’6 minutes
  • Getting to Stochastic Gradient Descent (SGD)β€’4 minutes
  • Comparing Gradient Descent and SGDβ€’6 minutes
  • Linear Regression with PyTorchβ€’24 minutes
  • Perceptrons and Neuronsβ€’8 minutes
  • Layers and Activations with torch.nnβ€’13 minutes
  • Multi-layer Feedforward Neural Networks (MLP)β€’9 minutes
2 assignmentsβ€’Total 60 minutes
  • The What, Why, and How of Generative AI Quizβ€’30 minutes
  • PyTorch for the Impatient Quizβ€’30 minutes

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268 Coursesβ€’65,144 learners

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