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Based on our record, OpenCV seems to be a lot more popular than MLJAR. While we know about 50 links to OpenCV, we've tracked only 3 mentions of MLJAR. 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.
Sure, at the bottom of our website you can subscribe for newsletter. Source: about 1 year ago
In my case, I had experience in DS and software engineering. It gives me ability to start a company that works on Data Science tools. Source: about 2 years ago
Instead, we started to work on desktop application that will allow to create python notebooks with no-code GUI (https://github.com/mljar/studio some screenshots on our website ). Source: over 2 years ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/). Source: 5 months ago
- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection. Source: 9 months ago
I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest... Source: 9 months ago
You also need C++ if you're going to do things which aren't built in as part of the engine. As an example if you're looking at using compute shaders, inbuilt native APIs such as a mobile phone's location services, or a third-party library such as OpenCV, then you're going to need C++. Source: 11 months ago
Google Cloud Machine Learning - Google Cloud Machine Learning is a service that enables user to easily build machine learning models, that work on any type of data, of any size.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Amazon Machine Learning - Machine learning made easy for developers of any skill level
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Teachable Machine - Easily create machine learning models for your apps, no coding required.
NumPy - NumPy is the fundamental package for scientific computing with Python