PyBrain
By pybrain
scikit-learn
scikit-learn: machine learning in Python (by scikit-learn)
| PyBrain | scikit-learn | |
|---|---|---|
| - | 91 | |
| 2,869 | 63,196 | |
| 0.0% | 0.6% | |
| 0.0 | 9.9 | |
| about 1 year ago | 1 day ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
PyBrain
Posts with mentions or reviews of PyBrain.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning PyBrain yet.
Tracking mentions began in Dec 2020.
scikit-learn
Posts with mentions or reviews of scikit-learn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-08-17.
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What is the Most Effective AI Tool for App Development Today?
For apps demanding robust machine learning capabilities, frameworks like TensorFlow provide the scalability and flexibility needed to handle large-scale data and models. These tools are essential for developers building features like recommendation engines or predictive analytics.
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Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
Machine learning (ML) teaches computers to learn from data, like predicting user clicks. Start with simple models like regression (predicting numbers) and clustering (grouping data). Deep learning uses neural networks for complex tasks, like image recognition in a Vue.js gallery. Tools like Scikit-learn and PyTorch make it easier.
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Predicting Tomorrow's Tremors: A Machine Learning Approach to Earthquake Nowcasting in California
Scikit-learn Documentation: https://scikit-learn.org/
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10 Useful Tools and Libraries for Python Developers
7. Scikit-learn - Machine Learning
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Must-Know 2025 Developer’s Roadmap and Key Programming Trends
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python, try projects that combine data with everyday problems. For example, build a simple recommendation system using Pandas and scikit-learn.
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🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
scikit-learn (optional): Useful for additional training or evaluation tasks.
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State of Python 3.13 Performance: Free-Threading
The race condition bugs are typically hidden by different software layers. For instance, we found one that involves OpenBLAS's pthreads-based thread pool management and maybe its scipy bindings:
- https://github.com/scipy/scipy/issues/21479
it might be the same as this one that further involves OpenMP code generated by Cython:
- https://github.com/scikit-learn/scikit-learn/issues/30151
We haven't managed to write minimal reproducers for either of those but as you can observe, those race conditions can only be triggered when composing many independently developed components.
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GitHub Repositories Every Developer Should Know: An In-Depth Guide
Visit the repository and explore examples.
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Essential Deep Learning Checklist: Best Practices Unveiled
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations.
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How to Build a Logistic Regression Model: A Spam-filter Tutorial
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 understanding the core concepts of logistic regression, its limitations, and exploring further resources, you'll be well-equipped to navigate the exciting world of machine learning!
What are some alternatives?
When comparing PyBrain and scikit-learn you can also consider the following projects:
Keras - Deep Learning for humans
Surprise - A Python scikit for building and analyzing recommender systems
tensorflow - An Open Source Machine Learning Framework for Everyone
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Pylearn2 - Warning: This project does not have any current developer. See bellow.