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Zoekresultaten (68)

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HBO/WO

MBO

23 augustus 2023

Lecture: Neural Networks

Learners understand the MLP architectre Learners can construct neural networks with fully connected layers and various activation functions Learners understand the basic idea of backpropagation and gradient-based methods

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HCAIM Consortium

23 augustus 2023

Lecture: Recurrent Neural Networks

Learning the fundamentals of Recurrent Neural Networks (RNN), Backpropagation Trough Time (BPTT) and Long Short-Term Memory (LSTM)

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HCAIM Consortium

5 oktober 2023

The Shallow and the Deep

The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon. Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility.

Open (Access) Tekst Boek

Michael Biehl

23 augustus 2023

Tutorial: Convolutional Neural Networks

Implementing and training a CNN for an image classification problem from scratch Fine-tuning of an already trained network Transfer learning using architectures trained on ImageNet

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HCAIM Consortium

23 augustus 2023

Lecture: Convolutional Neural Networks

To know what is a CNN and its main differences with Densely-connected NN To know the main difference between Locally-Connected Layers to Convolutional ones To know how to configure a CNN layer Pooling and batch normalization layers To know the most famous CNNs: LeNet, AlexNet, ResNet, VGG16, AllConvNet

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HCAIM Consortium

23 augustus 2023

Practical: Convolutional Neural Networks

To know what is a CNN and its main differences with Densely-connected NN To know the main difference between Locally-Connected Layers to Convolutional ones To know how to configure a CNN layer Pooling and batch normalization layers To know the most famous CNNs: LeNet, AlexNet, ResNet, VGG16, AllConvNet

Website

HCAIM Consortium

2021

Tracking student skills real-time through a continuous-variable dynamic Bayesian network

The field of Knowledge Tracing is focused on predicting the success rate of a student for a given skill. Modern methods like Deep Knowledge Tracing provide accurate estimates given enough data, but beingbased on neural networks they struggle to explain how these estimates are formed. More classical methodslike dynamic Bayesian networks can do this, but they cannot give data on the accuracy of their estimatesand often struggle to incorporate new observations in real-time due to their high computational load.This paper presents a novel method, Performance Distribution Tracing (PDT), in which the distribution of the success rate is traced. It uses a dynamic Bayesian network with continuous random variablesas nodes. By tracing the success rate distribution, there is always data available on the accuracy of any success rate estimation. In addition, it makes it possible to combine data from similar/related skills tocome up with a more informed estimate of success rates. This makes it possible to accurately predictexercise success rates, even when an exercise requires a combination of different skills to solve. Andthrough the use of the right conjugate priors, all distributions are available in analytical form, allowing efficient online updates upon new observations. An application to an engineering physics course has shown that the PDT algorithm works well inreal-life settings. Students mainly appreciate the overview given of their learning process as well as the corresponding practice recommendations.

Document

Hildo Bijl

23 augustus 2023

Tutorial: CNNs and Transformers for images

Implementing and training a CNN and a Transformer for a computer vision problem from scratch Comparing the performance of the two models

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HCAIM Consortium

23 augustus 2023

Lecture: Transformer networks

Learning the basics of sequence-to-sequence (seq2seq) models Learning the basics of attention mechanism Getting familiar with the transformers

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HCAIM Consortium

23 augustus 2023

Lecture: Building computational graphs, modern architectures

Understanding the fundamentals of Computational Graphs, residual connections, highway connections and skip connections

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HCAIM Consortium