Artificial Intelligence
with PyTorch
Artificial intelligence with PyTorch is the application of machine learning techniques and neural networks by using the PyTorch library to develop systems capable of learning from data and performing intelligent tasks automatically. In this course you will learn to understand the theoretical foundations of machine learning and neural networks, as well as how to use the PyTorch library to develop and train artificial intelligence models to solve real-world problems.
To who we address
The course on artificial intelligence with PyTorch is intended for a wide range of participants, including undergraduate students of computer science, engineering, and mathematics, as well as computer science professionals and machine learning engineers. Researchers and academics in the field of artificial intelligence might also find a course that presents the latest developments and research regarding PyTorch and machine learning useful.
Course structure
1
-
Introduction to PyTorch.
-
Exercise.
2
-
Neural networks with PyTorch.
-
Exercise.
3
-
Automatic differentiation in PyTorch.
-
Exercise, with analysis of use cases.
4
-
Training a neural network.
-
Exercise, with analysis of use cases.
5
-
Select of hyperparameters.
-
Exercise.
6
-
Hyperparameters management
-
Insights.