Deep Learning with PyTorch
Completed by Taha Enes Aslantürk
July 10, 2025
20 hours (approximately)
Taha Enes Aslantürk'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: Model Evaluation
- Category: PyTorch (Machine Learning Library)
- Category: Applied Machine Learning
- Category: Logistic Regression
- Category: Artificial Intelligence and Machine Learning (AI/ML)
- Category: Artificial Neural Networks
- Category: Convolutional Neural Networks
- Category: Model Training
- Category: Image Analysis
- Category: Classification Algorithms
- Category: Transfer Learning
- Category: Model Optimization
