Introduction to Neural Networks and PyTorch
Completed by Ming-Hua Sean Diep Lawrence
November 19, 2024
19 hours (approximately)
Ming-Hua Sean Diep Lawrence'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: Probability & Statistics
- Category: Deep Learning
- Category: Data Processing
- Category: Tensorflow
- Category: Applied Machine Learning
- Category: Supervised Learning
- Category: Machine Learning
- Category: Regression Analysis
- Category: Predictive Modeling
- Category: Statistical Methods
- Category: PyTorch (Machine Learning Library)
