Scikit-learn might be a bit more popular than Augur. We know about 27 links to it since March 2021 and only 22 links to Augur. 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.
People have brought new markets such as virtual lands and more to the limelight. Some of them include Kirin's learn-to-earn project, in which you can answer quizzes and tests to earn crypto assets, Aavegotchi and YOLOrekt's bid to earn. They are distinct, but they both perform the same B2E function. In Aavegotchi, the Aavegotchi B2E auction modifies at least one of the four personality traits of an Aavegotchi:... Source: over 1 year ago
Yes “handicappers” come up with the odds/spread for sports and some off the wall stuff. “Prediction markets” is a big thing in crypto. And yes you can create your own “outcome” for an event that people can bet on. If you wanna check out the most popular prediction market and see all the crazy things people are betting on here’s Augur https://augur.net/. Source: almost 2 years ago
It's not exactly new? There were several general betting market in crypto, but they never got much popularity. https://augur.net/ https://polymarket.com/. - Source: Hacker News / almost 2 years ago
Example of web 3 platforms are Steemit, Diaspora, Augur, Opensea, Everledger among many others while examples of Web 2 platforms are Facebook, Twitter, Binance, Upwork. - Source: dev.to / about 2 years ago
Https://augur.net (bets on real world events). Source: over 2 years 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 / 11 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: 12 months 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: 12 months 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 is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning... - Source: dev.to / about 1 year ago
TellApart - TellApart helps companies turn individual customer preferences into sales by predicting which items customers are most interested in
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
TradeX - TradeX is an exchange to bet on the predicted outcome of an upcoming event, like the number of covid cases, inflation rate, or the next president.
OpenCV - OpenCV is the world's biggest computer vision library
Predicto - Make predictions on the Blockchain
NumPy - NumPy is the fundamental package for scientific computing with Python