Generative AI with Diffusion Models (NVIDIA) Course Overview

Last Updated : 18 Apr 2025

Generative AI with Diffusion Models (NVIDIA) Course Overview

Unlock the potential of generative AI with our Generative AI with Diffusion Models (NVIDIA) course. This 16-hour program dives deep into denoising diffusion models—critical for text-to-image applications. Learn to build a U-Net that generates images from pure noise and enhance these images using advanced techniques in image denoising. Control image output with context embeddings and generate images from English text prompts using CLIP.

Learning Objectives:
- Build an image generation U-Net.
- Enhance images through the denoising process.
- Manipulate image outputs with embeddings.
- Generate text-based images using CLIP.

Practical Applications include creative content generation, data augmentation, and anomaly detection. Explore the transformative world of generative AI with us.

5.0

Intermediate

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Flexi Video	16,449
Official E-coursebook
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Hands-On-Labs²	4,159
+ GST 18%	4,259
Total Fees (without exam & Labs)	22,359 (INR)
Total Fees (with Labs)	28,359 (INR)

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Schedule personalized sessions based upon your availability.

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Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.

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Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

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Immerse yourself in a focused learning environment, free from distractions, where you can sharpen your skills in popular global destinations.

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Course Prerequisites

Prerequisites for Generative AI with Diffusion Models (NVIDIA) Course

To successfully undertake the Generative AI with Diffusion Models (NVIDIA) course, prospective learners should have the following minimum knowledge and prerequisites:

Basic Understanding of Neural Networks: Familiarity with fundamental neural network concepts and architectures.
Proficiency in Python Programming: Ability to write and understand Python code, as the course involves hands-on coding tasks.
Basic Knowledge of Machine Learning: Understanding of core machine learning concepts such as supervised learning, unsupervised learning, and overfitting.
Experience with Deep Learning Frameworks: Some experience with deep learning frameworks like TensorFlow, PyTorch, or similar tools.
Mathematical Foundations: Basic understanding of linear algebra, probability, and statistics as they relate to machine learning.

Having these foundational skills will help you successfully engage with and complete the course's content. If you need to brush up on any of these areas, we recommend reviewing relevant materials prior to starting the course.

Target Audience for Generative AI with Diffusion Models (NVIDIA)

Sure, here is the content as requested:

Take a deeper dive into denoising diffusion models and explore text-to-image pipelines with this 16-hour Generative AI course by NVIDIA, ideal for an array of tech professionals and innovators.

Data Scientists
AI Researchers
Machine Learning Engineers
Computer Vision Specialists
Software Developers interested in AI
Academic Researchers
AI Hobbyists
Creative Technologists
Pharmaceutical Researchers
Business Analysts in AI-driven industries
Product Managers in Tech
Simulation Engineers
Content Developers focused on AI Tools
Data Engineers
Computational Neuroscientists

Learning Objectives - What you will Learn in this Generative AI with Diffusion Models (NVIDIA)?

Generative AI with Diffusion Models (NVIDIA) Course: Learning Objectives and Outcomes

Introduction: This 16-hour course delves into denoising diffusion models for text-to-image generation and other applications. Learners will build and refine models to generate and enhance images from text prompts using advanced techniques.

Learning Objectives and Outcomes:

Build and understand the U-Net architecture to generate images from pure noise.
Improve the quality of generated images using the denoising diffusion process.
Control the image output with context embeddings.
Generate images from English text prompts using the Contrastive Language-Image Pretraining (CLIP) neural network.
Train models to remove noise and define forward and reverse diffusion functions.
Optimize U-Net architecture with Group Normalization, GELU, and Rearrange Pooling.
Implement Sinusoidal Position Embeddings and add categorical embeddings to a U-Net.
Train a model using Classifier-Free Diffusion Guidance with Bernoulli masks.
Use CLIP to encode text and create text-to-image neural networks.

Topics Covered:

U-Nets
Diffusion
CLIP
Text-to-image Models

Course Outline:

Build a U-Net architecture