scikit-learn
seqeval
Our great sponsors
| scikit-learn | seqeval | |
|---|---|---|
| 61 | 1 | |
| 52,699 | 886 | |
| 0.5% | 1.6% | |
| 9.9 | 0.0 | |
| 7 days ago | about 2 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT 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.
scikit-learn
-
Best Websites For Coders
Scikit-learn : A Python module for machine learning build on top of SciPy
-
scikit-learn VS Rath - a user suggested alternative
2 projects | 12 Jan 2023
-
Boston Dataset was Removed from scikit-learn 1.2
Can you really call this "banning the dataset"? https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
- ML Frameworks
-
Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
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?
If you look at the README for scikit-learn on GitHub, they say this.
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?
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)
scikit-learn
seqeval
We haven't tracked posts mentioning seqeval yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
Keras - Deep Learning for humans
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Surprise - A Python scikit for building and analyzing recommender systems
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.
PyBrain
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.
SciKit-Learn Laboratory - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration