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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 at the same time 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 MNIST dataset. You will then learn about convolutional neural networks, and use them to build much more powerful models which give more accurate results. You will evaluate the results and use different techniques to improve them. Following the course, you will be able to delve deeper into neural networks and start your career in this fascinating field.
Overview
Syllabus
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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.
