Introduction to Neural Networks and PyTorch
Completed by Parham Zilouchian Moghaddam
August 18, 2021
19 hours (approximately)
Parham Zilouchian Moghaddam's account is verified. Coursera certifies their successful completion of Introduction to Neural Networks and PyTorch
What you will learn
Develop foundational deep learning skills by building, training, and evaluating PyTorch models you can showcase in your professional portfolio
Gain practical experience with tensors, datasets, and automatic differentiation using PyTorch core tools including autograd and DataLoader
Develop linear regression models using gradient descent, mini-batch optimization, and training/validation splits to evaluate model performance
Apply cross-entropy loss, sigmoid-based classification, and advanced optimization techniques to build logistic regression models in PyTorch
Skills you will gain
- Category: Data Processing
- Category: Classification Algorithms
- Category: Tensorflow
- Category: Predictive Modeling
- Category: Model Optimization
- Category: Model Training
- Category: Regression Analysis
- Category: Predictive Analytics
- Category: PyTorch (Machine Learning Library)
- Category: Artificial Neural Networks
- Category: Machine Learning
- Category: Model Evaluation
