Introduction to Neural Networks and PyTorch
Completed by Mohamed Mahmoud Abdel-Wahab
February 22, 2020
19 hours (approximately)
Mohamed Mahmoud Abdel-Wahab'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: Applied Machine Learning
- Category: Tensorflow
- Category: Deep Learning
- Category: Probability & Statistics
- Category: Machine Learning Methods
- Category: Data Preprocessing
- Category: Supervised Learning
- Category: Regression Analysis
- Category: Logistic Regression
- Category: Statistical Methods
- Category: Machine Learning
- Category: Data Processing
