LaunchDarkly might be a bit more popular than Scikit-learn. We know about 37 links to it since March 2021 and only 28 links to Scikit-learn. 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.
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 3 months ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / 12 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
This kind of goes without saying since it's the opposite of the first don't I listed, but it's worth restating and giving some examples. Using tools from third parties means taking advantage of what they have done so you don't have to do that work. This means you are free to build things that make your app special. I like to use feature flag tools for this. Some examples are LaunchDarkly, Split, and AWS App... - Source: dev.to / 22 days ago
Taplytics is a broad A/B testing platform for marketing teams. While DevCycle is a feature flagging tool built for developers. Taplytics actually has feature flagging, but DevCycle is much more focused and plans to compete directly with incumbents like LaunchDarkly by building a better developer experience (more on how later). But with Taplytics they built so many features and every customer was using them in a... - Source: dev.to / 5 months ago
I had a custom rule added to Little Snitch that blocked the following domains: launchdarkly.com, clientstream.launchdarkly.com, mobile.launchdarkly.com. Source: 6 months ago
There are however Saas to implement directly a feature management system. Several solutions exist like LaunchDarkly, Flagsmith or Unleash.io. Using a SaaS (Software as a Service) feature flagging solution offers the advantage of a faster and more straightforward implementation process. These services are readily available and can be quickly integrated into your project. - Source: dev.to / 9 months ago
Currently, there are numerous feature flag systems available. Options include our own company's open-source system, "Bucketeer", and the renowned SaaS "LaunchDarkly" among others. When comparing these, the following considerations might come into play:. - Source: dev.to / 9 months ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
ConfigCat - ConfigCat is a developer-centric feature flag service with unlimited team size, awesome support, and a reasonable price tag.
OpenCV - OpenCV is the world's biggest computer vision library
Flagsmith - Flagsmith lets you manage feature flags and remote config across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features. We're Open Source.
NumPy - NumPy is the fundamental package for scientific computing with Python
Unleash - Open source Feature toggle/flag service. Helps developers decrease their time-to-market and to increase learning through experimentation.