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Related lectures (29)

Vision BeLearn: Learning the Future in the Centre

Explores the vision of BeLearn in digital education transformation and challenges, emphasizing the importance of adapting to digital changes in education.

Unsupervised Learning: Clustering Methods

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Fully Connected Networks on MNIST and SUSY Datasets

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Clustering Methods: K-means and Density Clustering

Explores k-means, kernel trick, and density clustering methods for non-convex clusters.

Advanced Clustering: DBSCAN

Covers Density-based Clustering (DBSCAN) and its algorithm step by step.

Making at School: Challenges and Projects

Explores challenges in education, maker-based teaching, and hands-on interdisciplinary projects in schools.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Document Segmentation: dhSegment

Explores the development and improvements of dhSegment, an open-source document segmentation package using PyTorch.

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