MSc in Artificial Intelligence for the University of Amsterdam.
Find Out MoreDeep learning is primarily a study of multi-layered neural networks, spanning over a great range of model architectures. This course is taught in the MSc program in Artificial Intelligence of the University of Amsterdam. In this course we study the theory of deep learning, namely of modern, multi-layered neural networks trained on big data. The course is taught by Assistant Professor Xiantong Zhen. The teaching assistants are Christos Athanasiadis, Ilze Amanda Auzina, Leonard Bereska, Jim Boelrijk, Natasha Butt, Mohammad Mahdi Derakhshani, Winfried van den Dool, Yingjun Du, Alex Gabel, Mariya Hendriksen, Tom Lieberum, Phillip Lippe, Yongtuo Liu, Jie Liu, Ben Miller, Ivona Najdenkoska, Sarah Rastegar, Nadja Rutsch, Mohammadreza Salehi, Jiayi Shen, Tom van Sonsbeek, Riccardo Valperga, Haochen Wang, Zehao Xiao
November 4, 2021 | 11.00-13.00 | Lecture
This lecture introduces the structure of the Deep Learning course, and gives a short overview of the history and motivation of Deep Learning.
November 4, 2021 | 13.00-15.00 | Tutorial session
This tutorial introduces the practical sessions, the TA organizer team, etc. Afterwards, we will discuss the PyTorch machine learning framework, and introduce you to the basic concepts of Tensors, computation graphs and GPU computation. We will continue with a small hands-on tutorial of building your own, first neural network in PyTorch.
We also provide a crash-course for working with the Lisa cluster, and how to setup your account for Lisa.
November 5, 2021 | 11.00-13.00 | Lecture
This lectures introduces basic concepts for Deep Feedforward Networks such linear and nonlinear modules, gradient-based learning and the backpropagation algorithm.
November 11, 2021 | 11.00-13.00 | Lecture
This lecture series discusses advanced optimizers, initialization, normalization and hyperparameter tuning.
November 11, 2021 | 13.00-15.00 | Tutorial session + TA Q&A
In this tutorial, we will discuss the role of activation functions in a neural network, and take a closer look at the optimization issues a poorly designed activation function can have.
After the presentation, there will by a TA session for Q&A for assignment 1, lecture content and more.
November 12, 2021 | 11.00-13.00 | Lecture
This lecture series discusses advanced optimizers, initialization, normalization and hyperparameter tuning.
November 18, 2021 | 11.00-13.00 | Lecture
This lecture series covers convolutional neural networks for image processing.
November 18, 2020 | 13.00-15.00 | Tutorial session + TA Q&A
In this tutorial, we will discuss the importance of proper parameter initialization in deep neural networks, and how we can find a suitable one for our network. In addition, we will review the optimizers SGD and Adam, and compare them on complex loss surfaces.
After the presentation, there will by a TA session for Q&A for assignment 1, lecture content and more.
November 19, 2021 | 11.00-13.00 | Lecture
This lecture series covers modern ConvNet architecture.
November 25, 2021 | 11.00-13.00 | Lecture
This lecture series covers Recurrent Neural Networks
November 25, 2020 | 13.00-15.00 | Tutorial session + TA Q&A
In this tutorial, we will implement three popular, modern ConvNet architectures: GoogleNet, ResNet, and DenseNet. We will compare them on the CIFAR10 dataset, and discuss the advantages that made them popular and successful across many tasks.
After the presentation, there will by a TA session for Q&A for assignment 2, lecture content and more.
November 26, 2021 | 13.00-15.00 | Online Lecture
Petar Veličković lecture on Graph Neural Networks.
December 2, 2021 | 11.00-13.00 | Lecture
Ivona's lecture on Attention and Transformers.
December 2, 2020 | 13.00-15.00 | Tutorial session + TA Q&A
In this tutorial, we will discuss the implementation of Graph Neural Networks. In the first part of the tutorial, we will implement the GCN and GAT layer ourselves. In the second part, we use PyTorch Geometric to look at node-level, edge-level and graph-level tasks.
After the presentation, there will by a TA session for Q&A for assignment 2, lecture content and more.
December 3, 2021 | 15.00-17.00 | Lecture
This lecture series discusses Generative Adversarial Networks.
December 9, 2021 | 11.00-13.00 | Lecture
This lecture series introduces the framework of variational inference and Variational Autoencoders (VAEs).
December 9, 2020 | 13.00-15.00 | Tutorial session + TA Q&A
In this tutorial, we will discuss the relatively new breakthrough architecture: Transformers. We will start from the basics of attention and multi-head attention, and build our own Transformer. We will perform experiments on sequence-to-sequence tasks and set anomaly detection.
After the presentation, there will by a TA session for Q&A for assignment 3, lecture content and more.
December 10, 2021 | 15.00-17.00 | Online Lecture
Deep Learning Generalization.
December 16, 2021 | 11.00-13.00 | Lecture
Geometry and Structure Regularized Deep Learning.
December 16, 2020 | 13.00-15.00 | Tutorial session + TA Q&A
In this tutorial, we will discuss deep convolutional autoencoders and their applications. In the practical and lecture, you will see variational autoencoders (VAE), which add a stochastic part to vanilla autoencoders. Both have their advantages and applications, of which we visit image retrieval and compression for the vanilla auotoencoder.
After the presentation, there will by a TA session for Q&A for assignment 3, lecture content and more.
December 17, 2021 | 11.00-13.00 | Lecture
Details will follow soon.
Deadline: November 19, 2021 Multilayer perceptrons and backpropagation Documents: |
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Deadline: December 3, 2021 CNNs, RNNs & Graph CNNs Documents: |
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Deadline: December 17, 2021 Generative Models Documents: |
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Some useful links for the course are the following:
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