|6 days ago||5 months ago|
|BSD 3-clause "New" or "Revised" License||BSD 3-clause "New" or "Revised" License|
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
2 projects | reddit.com/r/u_Matusadona_Wild303 | 25 Oct 2022
Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
5 projects | dev.to | 23 Oct 2022
The concepts are similar to the Scikit-learn project. They follow Spark’s “ease of use” characteristic giving you one more reason for adoption. You will learn more about these main concepts in this guide.
How do you programmers make sense of production-level code?
2 projects | reddit.com/r/learnprogramming | 20 Oct 2022
If you look at the README for scikit-learn on GitHub, they say this.2 projects | reddit.com/r/learnprogramming | 20 Oct 2022
Take a smaller segment to look at. Opening up the front page to a Github repo can be quite daunting. https://github.com/scikit-learn/scikit-learn
Talking Data: What do we need for engaging data analytics?
4 projects | dev.to | 6 Oct 2022
Many data workers are complaining about the fierce competition in the data area. Fortunately, the situation seems to be improving. Data analysts had to manually analyze distribution charts for deep insights, but now they can use smart machine learning models to automate this process. Traditional data analysis and modeling skills have been gradually becoming easy. For instance, Power BI or Tableau allow users to use a drag-and-drop low-code fashion to generate visual charts and models, whilst the old way is to import Python libraries such as pandas, matplotlib and sklearn to do the same in Jupyter Notebook. Open-source projects Apache Superset and Metabase allow users to easily analyze data on the web pages. This is quite similar to the development of digital cameras, from the film cameras to digital cameras and to smartphone cameras used by everyone. With lower and lower technical barriers, the whole industry can be developing fast. "Everyone can be data analyst" will no longer be a fantasy.
A few (unordered) thoughts about data (1/2)
6 projects | dev.to | 5 Oct 2022
Can anyone share some good examples of Python OOP Repos for DS?
4 projects | reddit.com/r/datascience | 17 Sep 2022
Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
8 projects | dev.to | 14 Aug 2022
scikit-learn – Simple and efficient tools for predictive data analysis, built on NumPy, SciPy, and matplotlib
Why do many data scientist use C++ for machine learning?
4 projects | reddit.com/r/learnmachinelearning | 29 Jul 2022
For example, there is PyTorch which is primarily C++ but has Python bindings. Most people use the Python bindings, same for TensorFlow. JAX is mostly Python, same for scikit-learn.
Don't Waste Data! An Experiment with Machine Learning
3 projects | dev.to | 23 Jun 2022
Once we had determined the shape of the data and the features we should focus on, we set out to create a model. (There is a wealth of ML tools available across programming languages like Python and Julia.) We chose scikit-learn, one of the most popular ML libraries around, and plugged the data into a random forest regression. (Say what? Here’s a quick and dirty guide to random forest regression.) As input, we used the ZIP codes of the print partner and the destination of the mailpiece. Our output target was the metric we had calculated during pre-processing: the difference in days between the earliest and latest USPS events recorded for each mailpiece (the mailpiece's time in transit).
We haven't tracked posts mentioning PyBrain yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
Keras - Deep Learning for humans
Surprise - A Python scikit for building and analyzing recommender systems
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
tensorflow - An Open Source Machine Learning Framework for Everyone
gensim - Topic Modelling for Humans
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
MLflow - Open source platform for the machine learning lifecycle
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.