Introduction to Neural Networks and PyTorch
Completed by Jin Ming, Jeremy Denzel Lim
March 27, 2020
19 hours (approximately)
Jin Ming, Jeremy Denzel Lim'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: PyTorch (Machine Learning Library)
- Category: Probability & Statistics
- Category: Regression Analysis
- Category: Statistical Methods
- Category: Logistic Regression
- Category: Predictive Modeling
- Category: Machine Learning Methods
- Category: Machine Learning
- Category: Data Preprocessing
- Category: Data Processing
- Category: Supervised Learning
- Category: Deep Learning
