Diffusion Models Skills you will learn

  • Understanding Diffusion Models
  • Sampling Techniques
  • Neural Network Architecture
  • Model Training and Optimization
  • Guided and Conditional Diffusion
  • Advanced Generative AI Techniques

Who should learn this free Diffusion Models course?

  • Generative AI Engineer
  • Machine Learning Engineer
  • Data Scientists
  • Deep Learning Specialists
  • NLP Engineer

What you will learn in this free Diffusion Models course?

  • Introduction to Diffusion Models

    • Introduction

      01:45
      • Introduction
        01:45

    • Lesson 1: What are Diffusion Models?

      04:16
      • What are Diffusion Models?
        04:16

    • Lesson 2: Objectives of Diffusion Models

      02:50
      • Objectives of Diffusion Models
        02:50

    • Lesson 3: Sampling in Diffusion Models

      09:20
      • Sampling in Diffusion Models
        09:20

    • Lesson 4: Neural Network Architecture

      04:14
      • Neural Network Architecture
        04:14

    • Lesson 5: Training a Neural Network

      11:32
      • Training a Neural Network
        11:32

    • Lesson 6: Controlling Diffusion Models

      10:43
      • Controlling Diffusion Models
        10:43

    • Lesson 7: Denoising Diffusion Implicit Models

      10:32
      • Denoising Diffusion Implicit Models
        10:32

    • Lesson 8: Conclusion

      05:57
      • Conclusion
        05:57

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Why you should learn Diffusion Models?

$356.05 Billion

Expected size of global Generative AI market by 2030.

$163K+ (USA) | INR 10.3 LPA

Average Salary of a Machine Learning Engineer annually.

About the Course

The Introduction to Diffusion Models course offers a deep dive into the exciting world of generative AI. This Diffusion Models course covers the foundational concepts, mathematical principles, and real-world applications of diffusion models in image and data generation. Participants will gain hands-on experience with popular frameworks like TensorFlow and PyTorch, learning to build, train, and optimize their own models. Ideal for AI enthusiasts, data scientists, and developers looking to expand their expertise in advanced generative techniques.

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FAQs

  • What are diffusion models?

    Diffusion models are a class of generative models used to create complex data distributions, often applied in image generation and other AI tasks.

  • Who should take this Diffusion Models course?

    This course is ideal for data scientists, AI enthusiasts, and developers interested in learning about generative models and their applications.

  • Do I need prior knowledge of AI to enroll in a Diffusion Models course?

    Basic understanding of machine learning and neural networks is recommended but not mandatory, as foundational concepts will be covered.

  • What topics will be covered in this Diffusion Models course?

    The course covers the fundamentals of diffusion models, mathematical underpinnings, model training, and practical applications in generative AI.

  • Will there be hands-on exercises?

    Yes, this Diffusion Models course includes practical exercises and coding tutorials to build and experiment with your own diffusion models.

  • What tools or software will be used in this Diffusion Models course?

    Popular frameworks like TensorFlow and PyTorch will be utilized for implementing diffusion models.

  • Is there a certification upon completion?

    Yes, you will receive a certificate, post-successful completion of this course.

  • How long is this Diffusion Models course?

    The course on Introduction to Diffusion Models is 2 hours long.

  • Are there any prerequisites for this Diffusion Models course?

    Familiarity with Python programming and basic machine learning concepts will be beneficial.

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