Nvidia Generative AI with Diffusion Models (GAIDM)
Ziele der Schulung
Thanks to improvements in computing power and scientific theory, generative AI is more accessible than ever before. Generative AI plays a significant role across industries due to its numerous applications, such as creative content generation, data augmentation, simulation and planning, anomaly detection, drug discovery, personalized recommendations, and more. In this course, learners will take a deeper dive into denoising diffusion models, which are a popular choice for text-to-image pipelines.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
- Build a U-Net to generate images from pure noise
- Improve the quality of generated images with 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
Zielgruppe Seminar
This course is aimed at machine learning engineers, AI researchers, and deep learning practitioners who are interested in image processing, generative models, and transformer-based models. It is particularly suited for professionals working in computer vision, image synthesis, and creative AI applications like text-to-image generation. The course assumes familiarity with deep learning concepts, neural networks (particularly U-Net), and frameworks like PyTorch. Ideal participants will have experience with basic image processing and model training, looking to explore diffusion models and advanced image generation techniques.
Voraussetzungen
- A basic understanding of Deep Learning Concepts.
- Familiarity with a Deep Learning framework such as TensorFlow, PyTorch, or Keras. This course uses PyTorch.
Lernmethodik
Die Schulung bietet Ihnen eine ausgewogene Mischung aus Theorie und Praxis in einer erstklassigen Lernumgebung. Profitieren Sie vom direkten Austausch mit unseren projekterfahrenen Trainern und anderen Teilnehmern, um Ihren Lernerfolg zu maximieren.
Seminarinhalt
From U-Net to Diffusion
- Build a U-Net architecture.
- Train a model to remove noise from an image.
Diffusion Model
- Define the forward diffusion function.
- Update the U-Net architecture to accommodate a timestep.
- Define a reverse diffusion function.
Optimizations
- Implement Group Normalization.
- Implement GELU.
- Implement Rearrange Pooling.
- Implement Sinusoidal Position Embeddings.
Classifier-Free Diffusion Guidance
- Add categorical embeddings to a U-Net.
- Train a model with a Bernoulli mask.
CLIP
- Learn how to use CLIP Encodings.
- Use CLIP to create a text-to-image neural network.
Hinweise
Partner
Dieses Seminar bieten wir in Kooperation mit unserem Nvidia Learning Partner Fast Lane Institute for Knowledge Transfer GmbH an.
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Übersicht: NVIDIA Schulungen Portfolio
