Introduction to Neural Networks and PyTorch
Completed by Sean Dickerson Springer
June 3, 2020
19 hours (approximately)
Sean Dickerson Springer'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: Supervised Learning
- Category: Data Processing
- Category: Tensorflow
- Category: Probability & Statistics
- Category: Deep Learning
- Category: Regression Analysis
- Category: Machine Learning
- Category: Statistical Methods
- Category: PyTorch (Machine Learning Library)
- Category: Applied Machine Learning
- Category: Predictive Modeling
