Notes from the Wired
This is a website where I write articles on various topics that interest me, carving out a bit of cyberspace for myself.
You shouldn't believe anything I talk about — I use words entirely recreationally.
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- May 23, 2025Info
I have based this commentary on the original German text as published by Reclam. The translation is my own, created with the assistance of ChatGPT.
Please note that this is purely my personal interpretation of the sermon. I have no formal training in theology or medieval studies, so my reading should be taken with a grain of salt.
Read moreMarch 25, 2025A year ago, a friend of mine had the idea to visit Namibia—often referred to as the “Gems of Africa” because of its diversity of animals and biomes. I’m not entirely sure how he came up with the idea. Maybe it was due to the country’s connection to Germany during its colonial period, or perhaps some algorithmic push from the “machine gods” in his feed. Whatever the reason, he asked our friend group if we were up for joining him. Another friend said yes, but I couldn’t go because it overlapped with some exams I had to take at university. However, I promised him that next semester, I would choose modules that allowed me to have some free time, which would overlap with theirs.
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Oct. 28
Deep Residual Learning for Image Recognition Paper Title: Deep Residual Learning for Image Recognition Link to Paper: https://arxiv.org/abs/1512.03385 Date: 10. Dec. 2015 Paper Type: Architecture, Learning Techniques, Deep Learning Short Abstract: Deeper networks are harder to train than shallower networks. In this paper, the authors introduce the technique of adding residual connections to the network, which drastically improves the performance of deeper networks. 1. Introduction Deep neural networks — that is, networks with many layers — have been used in many breakthroughs in machine learning. This raises the question: Is learning better networks as easy as stacking more layers?Oct. 27
AdamW: Decoupled Weight Decay Regularization Paper Title: AdamW: Decoupled Weight Decay Regularization Link to Paper: https://arxiv.org/abs/1711.05101 Date: 14. Nov. 2017 Paper Type: Optimizer, Learning Techniques, Deep Learning Short Abstract: This paper introduces the AdamW optimizer, an improvement on the Adam optimizer that additionally incorporates weight decay. 1. Introduction Adaptive, gradient-based optimizers such as AdaGrad, RMSprop, and Adam have become the default choice for training feed-forward neural networks. Still, state-of-the-art performance on many image datasets, such as CIFAR-10 and CIFAR-100, is often achieved using SGD.Oct. 27
Adam: A Method for Stochastic Optimization Paper Title: Adam: A Method for Stochastic Optimization Link to Paper: https://arxiv.org/abs/1412.6980 Date: 22. Dec. 2014 Paper Type: Optimizer, Learning Techniques, Deep Learning Short Abstract: In this paper, the famous Adam optimizer is introduced: a first-order, gradient-based optimization method that uses adaptive estimates of momentum. It generalizes well across different architectures and tasks and outperforms many optimizers that came before it. 1. Introduction Many problems in the fields of science and engineering can be formulated as the optimization of some scalar objective function requiring maximization or minimization.
