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Articles by Markus Stoll
Individualbesteuerung – klären Sie wichtige Fragen schon jetzt
Für Ehepaare ändert sich mit der Individualbesteuerung einiges. Antworten auf häufige Fragen und Tipps, die Sie jetzt schon umsetzen können. Die Individualbesteuerung bringt einen grossen Umbau des Steuersystems mit sich – sofern das Stimmvolk die noch hängige Initiative «Ja zu fairen Bundessteuern auch für Ehepaare» Ende November ablehnt. Prüfen Sie rechtzeitig, wie sich die neuen Regeln auf Ihre Steuern auswirken werden. Antworten auf die wichtigsten Fragen sind hier zusammengefasst.
Individualbesteuerung: Die Folgen für Säule 3a und Pensionskasse
Mit der Reform ändern sich die steuerlichen Rahmenbedingungen für PK‑Einkäufe und Kapitalbezüge. Das sollten Ehepaare beachten, um Nachteile zu vermeiden. Mit der Einführung der Individualbesteuerung muss künftig jeder Ehepartner eine eigene Steuererklärung ausfüllen. Das hat auch Folgen für den Bezug von Guthaben aus der Pensionskasse und Säule 3a sowie für freiwillige Einzahlungen in die Pensionskasse. Die Steuerreform betrifft vorerst die Bundessteuer.
Automatic AI based Test Data Evaluation
In the animation, such a similarity-based analysis is illustrated using telemetry data from a Formula 1 race. After each completed lap, all data from previous laps are evaluated and points are calculated based on the similarity map for their RPM profiles. Over time, patterns become visible, which can be used to identify unusual events that can then be inspected in more detail. Testing is essential for the development of safe and comfortable vehicles.
Top 5 Use Cases for AI in Automotive Testing Data Analysis
Noise, Vibration, Harshness (NVH) Acoustics Powertrain Electrical and Electronic (E/E) integration Advanced Driver Assistance Systems (ADAS) Passive Safety Electromagnetic Vulnerability (EMV) Vehicles are complex machines that rely on an intricate combination of mechanical components, electronics, and software to work properly. Quality Testing is key for making them reliable and safe.
Visualize RAG Data with Renumics-RAG - A Q&A GUI with Interactive Data Exploration
A Q&A GUI with interactive Data Exploration using UMAP to show Questions, Answers and their Relationships to Source Documents in a Similarity Map. Retrieval-Augmented Generation (RAG) is a technique that provides a retrieval step for Question Answering tasks for large Language Models (LLMs), allowing them to retrieve relevant data from a data source. This data source can be domain-specific data or private data.
Visualize your RAG Data — Evaluate your Retrieval-Augmented Generation System with Ragas Original
How to use UMAP dimensionality reduction for Embeddings to show multiple evaluation Questions and their relationships to source documents with Ragas, OpenAI, Langchain and ChromaDB Retrieval-Augmented Generation (RAG) adds a retrieval step to the workflow of an LLM, enabling it to query relevant data from additional sources like private documents when responding to questions and queries [1]. This workflow does not require costly training or fine-tuning of LLMs on the additional documents.
Visualize your RAG Data — EDA for Retrieval-Augmented Generation
How to use UMAP dimensionality reduction for Embeddings to show Questions, Answers and their relationships to source documents with OpenAI, Langchain and ChromaDB Large Language Models (LLMs) like GPT-4 have shown impressive power for text understanding and generation. But they face challenges handling domain-specific information. They tend to hallucinate incorrect answers when queries go beyond the training data [1].
🔍 How to Explore and Visualize ML-Data for Object Detection in Images
Use Ultralytics YOLOv8 detections and ViT embeddings to visualize and navigate the data in Renumics Spotlight 1.6.0 The need to understand ML-data in-depth is increasingly recognized. However, it is still not widely practiced in computer vision due to the large effort required to review large datasets. It is impossible to get a good understanding of the dataset by just clicking through images.
Hacktoberfest Machine Learning Projects for JS/TS Developers 🎃
Image generated with Midjourney by the author Finding machine learning projects that are suitable for JS/TS developers during Hacktoberfest can be daunting due to the overwhelming abundance of open-source projects. We've simplified this process, offering you a refined selection of opportunities where your coding skills can shine and make a real impact.
Image Classification in 2023
The use of pre-trained models on large datasets, such as ImageNet, followed by fine-tuning on specific target datasets, has become the default approach in image classification. This has radically simplified the task of image classification. In this article, we'll assemble an image dataset using a customizable search that can integrate our own data with Bing, then fine-tune a model, and evaluate and debug it using Spotlight for interactive visualization - all in just five lines of code.
How I Created an Animation Of the Embeddings During Fine-Tuning
Using Cleanlab, PCA, and Procrustes to visualize ViT fine-tuning on CIFAR-10 In the field of machine learning, Vision Transformers (ViT) are a type of model used for image classification. Unlike traditional convolutional neural networks, ViTs use the transformer architecture, which was originally designed for natural language processing tasks, to process images. Fine-tuning these models, for optimal performance can be a complex process.
Interactive Dataset Exploration for Image Classification
We are pleased to announce the release of Renumics Spotlight v1.2 on GitHub, which brings improved support for image classification datasets from Hugging Face: Cifar10 dataset [1] with embeddings visualized with github.com/renumics/spotlight - Screenshot from Demo at huggingface.co/spaces/renumics/cifar10-embeddings To begin, here is a code snippet to help you get started with Spotlight using the popular CIFAR-10 [1] dataset: import datasets from renumics import spotlight # choose any image...
Democratization of HPC Part 4: Deep Learning for Fluid Flow Prediction in the Cloud
This is the fourth and final article demonstrating the growing acceptance of high-performance computing (HPC) in new user communities and application areas. In this article we present UberCloud use case #211 on Deep Learning for Fluid Flow Prediction in the Advania Data Centers Cloud, for educational purposes, for our wider engineering simulation community.
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