Introduction to Neural Networks and PyTorch
Completed by JUAN DIEGO ECHAVARRIA RAMIREZ
November 16, 2021
19 hours (approximately)
JUAN DIEGO ECHAVARRIA RAMIREZ'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: Logistic Regression
- Category: PyTorch (Machine Learning Library)
- Category: Probability & Statistics
- Category: Data Processing
- Category: Tensorflow
- Category: Machine Learning Methods
- Category: Machine Learning
- Category: Deep Learning
- Category: Predictive Modeling
- Category: Data Preprocessing
- Category: Statistical Methods
