Deep Learning with PyTorch

Overview

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.

Syllabus

Introduction to PyTorch
-In this first chapter, we introduce basic concepts of neural networks and deep learning using PyTorch library.

Artificial Neural Networks
-In this second chapter, we delve deeper into Artificial Neural Networks, learning how to train them with real datasets.

Convolutional Neural Networks (CNNs)
-In this third chapter, we introduce convolutional neural networks, learning how to train them and how to use them to make predictions.

Using Convolutional Neural Networks
-In this last chapter, we learn how to make neural networks work well in practice, using concepts like regularization, batch-normalization and transfer learning.