Learn to create deep learning models with the PyTorch library.
Neural networks have been at the forefront of Artificial Intelligence research during the last few years and have provided solutions to many difficult problems like image classification, language translation or Alpha Go. PyTorch is one of the leading deep learning frameworks, being both powerful and easy to use. In this course, you will use PyTorch to first learn about the basic concepts of neural networks before building your first neural network to predict digits from an MNIST dataset.
You’ll start with an introduction to PyTorch, exploring the PyTorch library and its applications for neural networks and deep learning. Next, you’ll cover artificial neural networks and learn how to train them using real data.
As you progress through the course, you’ll learn about how to use convolutional neural networks to build much more powerful models which give more accurate results. You will evaluate the results and use different techniques to improve them. You'll also Cover concepts including regularization and transfer learning.
Following the course, you’ll have the confidence to delve deeper into neural networks and progress your knowledge further.
Neural networks have been at the forefront of Artificial Intelligence research during the last few years and have provided solutions to many difficult problems like image classification, language translation or Alpha Go. PyTorch is one of the leading deep learning frameworks, being both powerful and easy to use. In this course, you will use PyTorch to first learn about the basic concepts of neural networks before building your first neural network to predict digits from an MNIST dataset.
You’ll start with an introduction to PyTorch, exploring the PyTorch library and its applications for neural networks and deep learning. Next, you’ll cover artificial neural networks and learn how to train them using real data.
As you progress through the course, you’ll learn about how to use convolutional neural networks to build much more powerful models which give more accurate results. You will evaluate the results and use different techniques to improve them. You'll also Cover concepts including regularization and transfer learning.
Following the course, you’ll have the confidence to delve deeper into neural networks and progress your knowledge further.
