Introduction to Deep Learning with PyTorch
Learn to create deep learning models with the PyTorch library.
- 4 hours
- 17 Videos
- 53 Exercises
- 5,837 Participants
- 4,300 XP
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Course Description
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.
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1
Introduction to PyTorch
FreeIn this first chapter, we introduce basic concepts of neural networks and deep learning using PyTorch library.
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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.
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Artificial Neural Networks
In this second chapter, we delve deeper into Artificial Neural Networks, learning how to train them with real datasets.
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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.
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1
Introduction to PyTorch
FreeIn 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.
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Ismail Elezi
Researcher PHD Student at Ca' Foscari University of Venice
I am a third year PhD Student of Deep Learning, supervised by professor Marcello Pelillo at Ca' Foscari, University of Venice. During my PhD, I did an exchange period at ZHAW Datalab (Switzerland) working with professor Thilo Stadelmann. From January on, I am visiting professor's Laura Leal-Taixe lab in Technical University of Munich.
See MoreCollaborators
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Hadrien Lacroix
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Hillary Green-Lerman