callicon +1 210 504 8191
whatsAppIcon +1 210 415 9856
emailIcon info@koenig-solutions.com
koenig-logo
Login
whatsAppIcon +1 210 415 9856
phoneIcon +1 210 504 8191
koenig-logo
All Courses

Generative AI with Diffusion Models (NVIDIA) Course Overview

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.

img-google

5.0/5 Ratings

img-trustpilot Trustpilot

4.4/5 Ratings

Intermediate

Purchase This Course

Fee On Request

♱

  • Live Training (Duration : 16 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • Classroom Training fee on request
  • Select Date
    date-img
  • CST(united states) date-img

Select Time


♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

happinessGuaranteed_icon

Happiness Guaranteed

  • Live Training (Duration : 16 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

happinessGuaranteed_icon

Happiness Guaranteed

Request More Information

Email:  WhatsApp:

Koenig's Unique Offerings

images-1-1

1-on-1 Training

Schedule personalized sessions based upon your availability.

Learn More
images-1-1

Customized Training

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

Learn More
happinessGuaranteed_icon

Happiness Guaranteed

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

Learn More
images-1-1

Destination Training

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

Learn More
images-1-1

Fly-Me-A-Trainer (FMAT)

Flexible on-site learning for larger groups. Fly an expert to your location anywhere in the world.

Learn More

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

Suggested Courses

USD