Advanced PyTorch Techniques and Applications
Completed by Kwabena Kwayisi Kissiedu
September 5, 2025
11 hours (approximately)
Kwabena Kwayisi Kissiedu's account is verified. Coursera certifies their successful completion of Advanced PyTorch Techniques and Applications
What you will learn
Create and assess ML models for specific datasets, evaluating performance with proper metrics.
Design autoencoders for dimensionality reduction and build GANs for data simulation, analyzing quality.
Develop Graph Neural Networks for graph data and implement Transformers, including Vision Transformers.
Enhance models with semi-supervised learning using limited data, and deploy them with Flask on Google Cloud.
Skills you will gain
- Category: Supervised Learning
- Category: Model Optimization
- Category: Embeddings
- Category: Unsupervised Learning
- Category: PyTorch (Machine Learning Library)
- Category: Generative Adversarial Networks (GANs)
- Category: Model Evaluation
- Category: Network Model
- Category: Generative Model Architectures
- Category: Flask (Web Framework)
- Category: Model Deployment
- Category: Machine Learning Methods
