Vector Magic
Adobe Illustrator
Inkscape
Sketch
Affinity Designer
Gravit Designer
Autotracer.org
Vectorizer.io
Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
Figure Eight
Vector Magic PandasPandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.
Based on our record, Pandas should be more popular than Vector Magic. It has been mentiond 231 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I used this tool. I tried a number of them and this seemed the best: https://vectormagic.com/. - Source: Hacker News / over 1 year ago
I looked at a bunch of Vectorising tools, and in the end used https://vectormagic.com/. - Source: Hacker News / over 1 year ago
I think vector magic is the current state of the art: https://vectormagic.com/?=20 No one seems to have tried to leverage deep learning yet; either because they haven't thought of doing so, or it just wouldn't be worthwhile. Image to SVG's are an inherently deterministic task, with not much room for the noisy error of most deep learning models like stable diffusion and such. I think algorithmic approaches... - Source: Hacker News / over 2 years ago
The best pixel to vector is still vectormagic. They are on it since at least 2009 and have a native desktop app. I am not affiliated but just a bit flabbergasted that they are still so far ahead. https://vectormagic.com/. - Source: Hacker News / over 2 years ago
This is the most impressive raster to vector I have seen: https://vectormagic.com Vtracer doesn't seem to do as well. - Source: Hacker News / over 2 years ago
Feature transformations should be deterministic: The same input should produce the same output when the same feature definition and configuration are applied. This is what allows training, backtesting, and live inference to remain aligned. Tools such as Pandas, Spark, or feature platforms such as Feast can be used to implement that logic. - Source: dev.to / 2 months ago
For early-career security practitioners (0-3 years). Start with Python literacy if you do not have it. The free Python Crash Course book and the pandas getting-started guide are enough to bootstrap. Then a hands-on applied course: GTK Cyber's Applied Data Science & AI for Cybersecurity and SANS SEC595 are both reasonable starting points. The goal at this stage is to be able to load a Zeek conn.log into a pandas... - Source: dev.to / 3 months ago
Python and data engineering for security data. Pandas for ingesting Zeek, Sysmon, EDR, and SIEM exports. Timestamp normalization to UTC, join keys across heterogeneous sources, feature extraction from raw logs. Without this layer, the ML content downstream is theater. - Source: dev.to / 3 months ago
Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 3 months ago
Pandas url is the most widely used library for data manipulation. - Source: dev.to / 3 months ago
Adobe Illustrator - Adobe Illustrator is a vector graphics editor.
NumPy - NumPy is the fundamental package for scientific computing with Python
Inkscape - Inkscape is a free, open source professional vector graphics editor for Windows, Mac OS X and Linux.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Sketch - Professional digital design for Mac.
OpenCV - OpenCV is the world's biggest computer vision library