Deep Learning with PyTorch
Completed by Jimin Kim
January 21, 2026
20 hours (approximately)
Jimin Kim's account is verified. Coursera certifies their successful completion of Deep Learning with PyTorch
What you will learn
Get hands-on experience using PyTorch to build and deploy AI systems and complete a portfolio-worthy project.
Develop and train shallow neural networks with various architectures and apply Softmax regression in multi-class classification problems.
Explore deep neural networks, including techniques such as dropout, weight initialization, and batch normalization.
Gain practical experience with convolutional neural networks, exploring layers, activation functions, and more.
Skills you will gain
- Category: Artificial Neural Networks
- Category: Artificial Intelligence and Machine Learning (AI/ML)
- Category: Model Optimization
- Category: Machine Learning
- Category: Statistical Methods
- Category: Supervised Learning
- Category: PyTorch (Machine Learning Library)
- Category: Classification Algorithms
- Category: Computer Vision
- Category: Applied Machine Learning
- Category: Image Analysis
- Category: Deep Learning
