Introduction to Neural Networks and PyTorch
Completed by Abdul Wahab Amin
December 9, 2019
19 hours (approximately)
Abdul Wahab Amin'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: Predictive Modeling
- Category: Supervised Learning
- Category: Probability & Statistics
- Category: Deep Learning
- Category: Tensorflow
- Category: Data Processing
- Category: Machine Learning
- Category: Regression Analysis
- Category: Applied Machine Learning
- Category: Statistical Methods
- Category: PyTorch (Machine Learning Library)
