GPU.LAND
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Scikit-learn
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Based on our record, Scikit-learn should be more popular than GPU.LAND. It has been mentiond 28 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'm just going to mention here the experience of someone who ran gpu.land (doesn't exist any more). He did something similar, monetized it (very cheap) and then had to shut down because people were running crypto miners on it. I hope you have a plan to avoid that type of abuse. Source: about 2 years ago
RIP to gpu.land... I was hoping they would take off because they seemed to have a cool product with great pricing. Source: almost 3 years ago
There's also https://gpu.land (which has their own comparison page). Source: about 3 years ago
Heya, I'm also so just keeping in touch. After liek 1 month of non redditing, someone replied who claimed to be the developer of gpu.land Apparently it is cloud computing for full Linux rather than the Jupyter notebook like what we tried before. Can I ask what is the update on the cloud computing site? I messaged the gpu.land person to see if we can get some free trial ($1 per hour on cheapest one but I don't know... Source: about 3 years ago
There are also more affordable GPU-for-DL-lending options like gpu.land, although I have never used them so I can't vouch for them -- just something I saw on PH. Source: about 3 years ago
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
Banana.dev - Banana provides inference hosting for ML models in three easy steps and a single line of code.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Clever Grid - Easy to use and fairly priced GPUs for Machine Learning
OpenCV - OpenCV is the world's biggest computer vision library
Apple Core ML - Integrate a broad variety of ML model types into your app
NumPy - NumPy is the fundamental package for scientific computing with Python