MLlib VS OpenCV
Compare MLlib VS OpenCV and see what are their differences
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
featured
MLlib
Scikit-learn
Pandas
NumPy
Dataiku
Exploratory
htm.java
WEKA
OpenCV
Scikit-learn
Pandas
NumPy
Dataiku
Exploratory
htm.java
WEKA
MLlib OpenCVBased on our record, OpenCV seems to be a lot more popular than MLlib. While we know about 50 links to OpenCV, we've tracked only 2 mentions of MLlib. 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.
The MLlib library gives us a very wide range of available Machine Learning algorithms and additional tools for standardisation, tokenisation and many others (for more information visit the official website Apache Spark MLlib). (Apache Spark Machine Learning predicting diabetes in patients). Source: about 2 years ago
Totally agree with the current responses, especially for the purposes of understanding exactly what's going on under the hood, but did want to just call out the fact that you can simply use a machine learning library that's implemented in a distributed way. Examples would be MLlib From Spark and h2o. H2O in particular will take care of pretty much everything for you in terms of initializing a cluster, and has a... Source: about 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: 12 months ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
NumPy - NumPy is the fundamental package for scientific computing with Python
Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.
Exploratory - Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.
htm.java - htm.java is a Hierarchical Temporal Memory implementation in Java, it provide a Java version of NuPIC that has a 1-to-1 correspondence to all systems, functionality and tests provided by Numenta's open source implementation.