I love building things. This blog in itself is one of these things. Feel free to shoot me an email.
Posts
-
Statistical Learning Theory
These are exam preparation notes, subpar in quality and certainly not of divine quality. See the index with all articles in this series In a classification problem the desired goal is to reduce the generalization error \(E^G\). Unfortunately during training it is only possible to evaluate the classifier against a limited amount of data - the test data set. Therefore we can only measure \(E^T\). The problem we want to...
Read more...
-
34C3 Economics of Climate Change Lightning Talk
Your browser does not support the video tag. Youtube mirror
Read more...
-
Docker Images with Gitlab CI
You want to have docker tags that match your git branches? Here is how to do it with Gitlab CI. A lot of my projects have a CI pipeline that builds a docker image. Of course I do not want to always deploy the :latest tag, because that makes reproducibility and rollbacks hard. I always push to :latest. Also I want to reference by: tags/branches commit hash For this repo...
Read more...
-
34C3 Day 1
Day one of the 34C3 is over. The new location in Leipzig is a lot more spacey and loftey, but I liked the old location in the CCH more. Somehow I felt that there were fewer easter-eggs and hidden nuggets than in previous congresses. I guess everyone still needs to adapt to the new environment. Hopefully in the coming days there will be more. Tomorrow will also be my first...
Read more...
-
Estimation theory - Kernel Density Estimation
Chapters General Terms and tools PCA PCA Hebbian Learning Kernel-PCA Source Separation ICA Infomax ICA Second Order Source Separation FastICA Stochastic Optimization Clustering k-means Clustering Pairwise Clustering Self-Organising Maps Locally Linear Embedding Estimation Theory Density Estimation Kernel Density Estimation Parametric Density Estimation Mixture Models - Estimation Models Density Estimation The goal of density estimation is to be able to give a density estimation for each coordinate in the vector space....
Read more...
-
Stochastic Optimization
Chapters General Terms and tools PCA PCA Hebbian Learning Kernel-PCA Source Separation ICA Infomax ICA Second Order Source Separation FastICA Stochastic Optimization Clustering k-means Clustering Pairwise Clustering Self-Organising Maps Locally Linear Embedding Estimation Theory Density Estimation Kernel Density Estimation Parametric Density Estimation Mixture Models - Estimation Models Simulated Annealing Simulated annealing is oriented in crystallization procedures in nature where the lowest energy state is achieved only when the temperature is...
Read more...
-
Clustering - k-means & SOM
Chapters General Terms and tools PCA PCA Hebbian Learning Kernel-PCA Source Separation ICA Infomax ICA Second Order Source Separation FastICA Stochastic Optimization Clustering k-means Clustering Pairwise Clustering Self-Organising Maps Locally Linear Embedding Estimation Theory Density Estimation Kernel Density Estimation Parametric Density Estimation Mixture Models - Estimation Models K-means Clustering K-means Clustering is good at finding equally sized clusters of data points. Parameters Distance Function (Usually Euclidean) Number of clusters Drawbacks...
Read more...
-
Source Separation (ICA)
Chapters General Terms and tools PCA PCA Hebbian Learning Kernel-PCA Source Separation ICA Infomax ICA Second Order Source Separation FastICA Stochastic Optimization Clustering k-means Clustering Pairwise Clustering Self-Organising Maps Locally Linear Embedding Estimation Theory Density Estimation Kernel Density Estimation Parametric Density Estimation Mixture Models - Estimation Models Independent Component Analysis (ICA) ICA allows the reconstruction of mixed signals. This could for example be multiple speakers on one audio track. Requirements...
Read more...