R
- ML models: What they can’t learn
- openrouteservice – geodata
- broom and dplyr for exploratory k-means clustering
- Understanding PCA using Stack Overflow data
- To purrr or not to purrr
- furrr: Apply Mapping Functions in Parallel using Futures
- ggplot2 2.3.0 — upcoming release
Python
- How to easily do Topic Modeling with LSA, PLSA, LDA & lda2Vec – a comprehensive overview of Topic Modeling and its associated techniques
- A NumPy-compatible matrix library accelerated by CUDA
- Yellowbrick – Visual analysis and diagnostic tools to facilitate machine learning model selection
- NLP Architect by Intel AI Lab: Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding
- Generating Climate Temperature Spirals in Python
- cartopy — Cartopy is a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses
- Implementing LDA in Python with Scikit-Learn
- Creating Interactive PDF Forms in ReportLab with Python
- Filling PDF Forms with Python
Interesting articles, projects and news
- Self-driving technology is going to change a lot more than cars
- Polizei ermittelt Täter über Gendatenbank von Ahnenforschern
- From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox
- Facebook weiß nicht, welche Daten genau Cambridge Analytica hatte
- Stanford-Professor warnt vor Public Clouds: “Jeff Bezos will das Universum beherrschen”
- Bitcoin’s stupendous power waste is green, apparently — bad excuses for Proof-of-Work
- Umfrage zu Algorithmen: Große Mehrheit für Verbot vollautomatisierter Entscheidungen
- AI and Compute
- We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.5 month-doubling time (by comparison, Moore’s Law had an 18-month doubling period)
- Studie zum Bitcoin: Energieverbrauch der Miner steigt auf immense Höhen
- Diskussionen zwischen KI-Systemen sollen Entscheidungen nachvollziehbar machen
- Rethinking Academic Data Sharing
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