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PyTorch Advanced Architectures and Deployment
Coursera
Course
Unknown

PyTorch Advanced Architectures and Deployment

DeepLearning.AI

Enhance PyTorch skills by designing advanced deep learning models including ResNet, Siamese, and Transformers; and learn model compression and deployment techniques.

Unknown4 weeksEnglish

About this Course

Advance your PyTorch skills by building sophisticated deep learning models and preparing them for deployment. You’ll design custom architectures that go beyond Sequential models, exploring Siamese Networks, ResNet, and DenseNet to understand how modern systems handle complex data. You’ll build Transformer architectures and explore how attention mechanisms power modern language models. You’ll also learn how diffusion models generate realistic images by reversing noise. Along the way, you’ll vis

What You'll Learn

  • Design and implement advanced architectures in PyTorch
  • Apply advanced techniques in vision, language, and generative modeling—including Transformers and diffusion models
  • Prepare, compress, and deploy models for real-world use

Prerequisites

  • Intermediate PyTorch knowledge
  • Deep learning concepts

Instructors

L

Laurence Moroney

Topics

Deep Learning
Software Visualization
PyTorch (Machine Learning Library)
MLOps (Machine Learning Operations)
Model Deployment
Computer Vision
Generative AI
Model Evaluation
Transfer Learning
Convolutional Neural Networks

Course Info

PlatformCoursera
LevelUnknown
PacingUnknown
PriceFree

Skills

بايتورتش
تعلم عميق متقدم
نشر النماذج
الرؤية الحاسوبية
الذكاء الاصطناعي التوليدي
تصميم النماذج
تقييم النماذج
عمليات تعلم الآلة
Transfer Learning
Convolutional Neural Networks

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