Self-driving cars, smartphones, search engines... Deep learning is now everywhere. Before you begin building complex models, you will become familiar with PyTorch, a deep learning framework. You will learn how to manipulate tensors, create PyTorch data structures, and build your first neural network in PyTorch with linear layers.

Introductie tot deep learning met PyTorch

Aan de slag met PyTorch-tensors

Tensors controleren en optellen

Neurale netwerken en lagen

Netwerk met lineaire laag

Gewichten begrijpen

Verborgen lagen en parameters

Je eerste neurale netwerk

Neuraal netwerk

Lineaire lagen stapelen

Het aantal parameters tellen

Introduction to PyTorch, a Deep Learning Library

To train a neural network in PyTorch, you will first need to understand additional components, such as activation and loss functions. You will then realize that training a network requires minimizing that loss function, which is done by calculating gradients. You will learn how to use these gradients to update your model's parameters.


Discovering activation functions

Activate your understanding!

The sigmoid and softmax functions

Running a forward pass

Building a binary classifier in PyTorch

From regression to multi-class classification

Using loss functions to assess model predictions

Creating one-hot encoded labels

Calculating cross entropy loss

Using derivatives to update model parameters

Accessing the model parameters

Updating the weights manually

Using the PyTorch optimizer

Neural Network Architecture and Hyperparameters

Now that you've learned the key components of a neural network, you'll train one using a training loop. You'll explore potential issues like vanishing gradients and learn strategies to address them, such as alternative activation functions and tuning learning rate and momentum.

A deeper dive into loading data

Using TensorDataset

Using DataLoader

Writing our first training loop

Using the MSELoss

Writing a training loop

ReLU activation functions

Implementing ReLU

Implementing leaky ReLU

Understanding activation functions

Learning rate and momentum

Experimenting with learning rate

Experimenting with momentum

Training a Neural Network with PyTorch

Training a deep learning model is an art, and to make sure our model is trained correctly, we need to keep track of certain metrics during training, such as the loss or the accuracy. We will learn how to calculate such metrics and how to reduce overfitting.


Layer initialization and transfer learning

Fine-tuning process

Freeze layers of a model

Layer initialization

Evaluating model performance

Writing the evaluation loop

Calculating accuracy using torchmetrics

Fighting overfitting

Experimenting with dropout

Understanding overfitting

Improving model performance

Implementing random search

Wrap-up video

Evaluating and Improving Models

Water Potability

Face Mask Dataset

Course Glossary

Deep learning is overal, van smartphonecamera's tot spraakassistenten en zelfrijdende auto's. In deze cursus ontdek je deze krachtige technologie en leer je hoe je haar inzet met PyTorch, een van de populairste deep-learningbibliotheken. Aan het einde van deze cursus kun je PyTorch gebruiken om classificatie- en regressieproblemen op te lossen met deep learning.

De video's bevatten live-transcripten die je kunt tonen door linksonder op de video's op "Show transcript" te klikken.

De begrippenlijst van de cursus vind je rechts in de sectie met bronnen.

Om CPE-credits te behalen moet je de cursus afronden en minstens 70% scoren op de gekwalificeerde toets. Je kunt naar de toets gaan door rechts op de CPE-credits-callout te klikken.

Supervised Learning with scikit-learn

Introduction to NumPy

Python Toolbox

Leer hoe je je eerste neuraal netwerk bouwt, hyperparameters aanpast en problemen oplost in PyTorch.

Neurale netwerken en lagen

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