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Deep Learning with PyTorch

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4 hours
4,300 XP
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Course Description

Discover Deep Learning with PyTorch

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.

Explore Deep Learning Models

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.

Learn to Use Neural Networks

As you progress through the course, you’ll 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. 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.
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  1. 1

    Introduction to PyTorch

    Free

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

    Play Chapter Now
    Introduction to PyTorch
    50 xp
    Creating tensors in PyTorch
    100 xp
    Matrix multiplication
    100 xp
    Forward propagation
    50 xp
    Forward pass
    100 xp
    Backpropagation by auto-differentiation
    50 xp
    Backpropagation by hand
    50 xp
    Backpropagation using PyTorch
    100 xp
    Calculating gradients in PyTorch
    100 xp
    Introduction to Neural Networks
    50 xp
    Your first neural network
    100 xp
    Your first PyTorch neural network
    100 xp
  2. 2

    Artificial Neural Networks

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

    Play Chapter Now
    Activation functions
    50 xp
    Neural networks
    100 xp
    ReLU activation
    100 xp
    ReLU activation again
    100 xp
    Loss functions
    50 xp
    Calculating loss function by hand
    50 xp
    Calculating loss function in PyTorch
    100 xp
    Loss function of random scores
    100 xp
    Preparing a dataset in PyTorch
    50 xp
    Preparing MNIST dataset
    100 xp
    Inspecting the dataloaders
    100 xp
    Training neural networks
    50 xp
    Building a neural network - again
    100 xp
    Training a neural network
    100 xp
    Using the network to make predictions
    100 xp
  3. 3

    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.

    Play Chapter Now
    Convolution operator
    50 xp
    Convolution operator - OOP way
    100 xp
    Convolution operator - Functional way
    100 xp
    Pooling operators
    50 xp
    Max-pooling operator
    100 xp
    Average-pooling operator
    100 xp
    Convolutional Neural Networks
    50 xp
    Your first CNN - __init__ method
    100 xp
    Your first CNN - forward() method
    100 xp
    Training Convolutional Neural Networks
    50 xp
    Training CNNs
    100 xp
    Using CNNs to make predictions
    100 xp
  4. 4

    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.

    Play Chapter Now
    The sequential module
    50 xp
    Sequential module - init method
    100 xp
    Sequential module - forward() method
    100 xp
    The problem of overfitting
    50 xp
    Validation set
    100 xp
    Detecting overfitting
    50 xp
    Regularization techniques
    50 xp
    L2-regularization
    100 xp
    Dropout
    100 xp
    Batch-normalization
    100 xp
    Transfer learning
    50 xp
    Finetuning a CNN
    100 xp
    Torchvision module
    100 xp
    Congratulations!
    50 xp
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Collaborators

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Hadrien Lacroix
Collaborator's avatar
Hillary Green-Lerman

Prerequisites

Object-Oriented Programming in PythonSupervised Learning with scikit-learn
Ismail Elezi HeadshotIsmail 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.
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