|12 months ago||5 days ago|
|MIT 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.
We haven't tracked posts mentioning neptune-contrib yet.
Tracking mentions began in Dec 2020.
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).
[D] Looking for a python library that implements decision tree regressors handling categorical features
2 projects | reddit.com/r/MachineLearning | 8 May 2022
Perhaps this would be of interest to you: NOCATS
Desmistificando roteirizações com Python
4 projects | dev.to | 23 Mar 2022
Python for everyone :Mastering Python The Right Way
3 projects | dev.to | 1 Mar 2022
http://scikit-learn.org/ - Machine learning with Python https://www.tensorflow.org/ - Deep learning with Python https://www.djangoproject.com/ - https://www.python.org/dev/peps/pep-0008
Identifying trolls and bots on Reddit with machine learning (Part 2) - Identificando trolls y bots en reddit con Machine Learning
5 projects | reddit.com/r/Republica_Argentina | 17 Dec 2021
Our next step is to create a new machine learning model based on this list. We’ll use Python’s excellent scikit learn framework to build our model. We’ll store our training data into two data frames: one for the set of features to train in and the second with the desired class labels. We’ll then split our dataset into 70% training data and 30% test data.
Old guy programmer here, need to brush up on Python quickly!
13 projects | reddit.com/r/Python | 6 Dec 2021
scikit-learn for classical machine learning,
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.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration