I love building things. This blog in itself is one of these things. Feel free to shoot me an email.
Posts
-
Reinforcement Learning
These are exam preparation notes, subpar in quality and certainly not of divine quality. See the index with all articles in this series In reinforcement learning an actor is in a world where she can perform different actions and perceive the environment. Sometimes there may be rewards. Reinforcement learning is about choosing a policy from which to derive actions that maximize the reward. Just like the real world there are...
Read more...
-
Support Vector Machines
These are exam preparation notes, subpar in quality and certainly not of divine quality. See the index with all articles in this series If you are stuck, read Wikipedia in parallel. The goal of SVMs is to divide two groups with a line that separates the data points as clearly as possible. There are two cases: Data points can be cleanly split into their classes At least some data points...
Read more...
-
Performance measures
These are exam preparation notes, subpar in quality and certainly not of divine quality. See the index with all articles in this series Choice of error function Usually squared error is used. Cross-Entropy \(c\) := different classes (classification / symbol representation) From Wikipedia: In information theory, the cross entropy between two probability distributions p and q over the same underlying set of events measures the average number of bits needed...
Read more...
-
Neural Networks
These are exam preparation notes, subpar in quality and certainly not of divine quality. See the index with all articles in this series Connectionist Neurons A neural network generally has a number of inputs \(x_1...x_N\) which are aggregated into \(\underline x\). At each node there is a transfer function \(y_i\) which turns the inputs according to weights \(\underline w\) into its own output. A typical function would look like this....
Read more...
-
Bayesian Networks
These are exam preparation notes, subpar in quality and certainly not of divine quality. See the index with all articles in this series Bayes Rule \[P(A|B) = \frac{P(B|A)P(A)}{P(B)}\] Inference Bayesian Interference is about inferring probabilities from prior probabilities. Given a set of prior events, a bayesian network estimates the probability for another event. Justification of using heuristics: In real-world scenarios you never know the true probability of events. To somehow...
Read more...
-
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...