Introduction to Neural Networks and PyTorch
Completed by Michael Rybalkin
January 4, 2024
19 hours (approximately)
Michael Rybalkin's account is verified. Coursera certifies their successful completion of Introduction to Neural Networks and PyTorch
What you will learn
Get hands-on 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: Regression Analysis
- Category: Applied Machine Learning
- Category: Supervised Learning
- Category: Machine Learning
- Category: Tensorflow
- Category: Probability & Statistics
- Category: Deep Learning
- Category: Predictive Modeling
- Category: Data Processing
- Category: Statistical Methods
- Category: PyTorch (Machine Learning Library)
