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GNU Octave VS machine-learning in Python

Compare GNU Octave VS machine-learning in Python and see what are their differences

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  3. » Reviews
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GNU Octave logo GNU Octave

GNU Octave is a programming language for scientific computing.

machine-learning in Python logo machine-learning in Python

Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.
  • GNU Octave Landing page
    Landing page //
    2022-08-07
  • machine-learning in Python Landing page
    Landing page //
    2020-01-13

GNU Octave features and specs

  • Free and Open Source
    GNU Octave is completely free to use and distribute. Its source code is available for anyone to inspect, modify, and enhance, providing transparency and community-driven improvements.
  • MATLAB Compatibility
    Octave aims to be mostly compatible with MATLAB, meaning that many scripts and functions written for MATLAB can run in Octave with little or no modification.
  • Extensive Documentation
    Octave has comprehensive documentation, tutorials, and a vast array of user-contributed content, easing the learning curve for new users.
  • Flexible Integration
    Octave can interface with various programming languages such as C, C++, Fortran, and Python, making it versatile for different types of projects and workflows.
  • Powerful Plotting Capabilities
    Octave includes features for generating high-quality plots and visualizations, which are essential for data analysis and presentation.

Possible disadvantages of GNU Octave

  • Performance
    In some cases, Octave may be slower than MATLAB, especially for highly optimized or proprietary algorithms that MATLAB handles more efficiently.
  • GUI and Toolboxes
    While Octave offers a graphical user interface, it is not as polished as MATLAB's. Additionally, the range and quality of toolboxes available in Octave can be more limited compared to MATLAB's extensive and well-supported toolboxes.
  • Community Support
    Although there is a supportive community around Octave, the user base and available support resources are smaller compared to MATLAB's extensive network of forums, user groups, and customer support.
  • Learning Curve for Advanced Features
    While basic operations are straightforward, mastering advanced features and customizations in Octave can require a deeper understanding of its architecture and available functions.
  • Less Industry Adoption
    MATLAB is widely used in industry for research, engineering, and analytics. Octave, being an open-source alternative, lacks the same level of commercial adoption and institutional support, which can be a drawback in professional settings.

machine-learning in Python features and specs

  • Ease of Use
    Python has a simple and clean syntax, which makes it accessible for beginners and efficient for experienced developers to implement fundamental concepts of machine learning quickly.
  • Rich Ecosystem
    Python boasts a vast collection of libraries and frameworks such as scikit-learn, TensorFlow, and PyTorch that provide extensive functionalities for machine learning tasks.
  • Community Support
    Python has a large and active community that contributes to continuous improvement, support, and readily available resources like tutorials, forums, and documentation for troubleshooting.
  • Integration Capabilities
    Python can easily integrate with other languages and technologies, enabling seamless deployment of machine learning models in diverse environments.
  • Visualization Tools
    Python supports various visualization libraries like Matplotlib and Seaborn which are crucial for data analysis and understanding the performance of machine learning models.

Possible disadvantages of machine-learning in Python

  • Performance Limitations
    Python is an interpreted language and can be slower compared to compiled languages like C++ or Java, which might be a consideration for performance-intensive tasks.
  • Global Interpreter Lock (GIL)
    The GIL in Python can be a bottleneck for multi-threaded applications, limiting parallel execution and performance in CPU-bound machine learning tasks.
  • Dependency Management
    Managing dependencies can be complex in Python projects, especially when handling different versions of libraries required for specific machine learning projects.
  • Memory Consumption
    Python can require more memory for large datasets when compared with more memory-efficient languages, which might affect scalability and the ability to process very large datasets.

GNU Octave videos

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GNU Octave Ep. 1.5: What's different compared to MatLab!

machine-learning in Python videos

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Category Popularity

0-100% (relative to GNU Octave and machine-learning in Python)

GNU Octave

machine-learning in Python

Technical Computing
96 96%
Technical Computing
4% 4
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Numerical Computation
100 100%
Numerical Computation
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare GNU Octave and machine-learning in Python

GNU Octave Reviews

7 Best MATLAB alternatives for Linux
FreeMAT is a free and open-source software for numerical computation. It is used for rapid engineering, scientific prototyping, and data processing. It is similar to MATLAB and GNU Octave and supports its various functions.
Source: cyanogenmods.org
Matlab Alternatives
Scilab is an open-source similar to the implementation of Matlab. The approximation techniques known as Scientific Computing is used to solve numerical problems. To achieve this, the team of Scilab developers made use of Solvers and algorithms to build the algebraic libraries. Scilab is one of the major alternatives to Matlab along with GNU Octave.
Source: www.educba.com
10 Best MATLAB Alternatives [For Beginners and Professionals]
GNU Octave an open-source alternative to MATLAB. It is interactive and powerful featuring everything you need in one place.
Source: thetechtop10.com
4 open source alternatives to MATLAB
GNU Octave may be the best-known alternative to MATLAB. In active development for almost three decades, Octave runs on Linux, Windows, and Mac—and is packaged for most major distributions. If you're looking for a project that is as close to the actual MATLAB language as possible, Octave may be a good fit for you; it strives for exact compatibility, so many of your projects...
Source: opensource.com
3 Open Source Alternatives to MATLAB
GNU Octave may be the best-known alternatives to MATLAB. In active development for almost three decades, Octave runs on Windows, Mac, and Linux alike, and is packaged for most major distributions. If you're looking for a project that is as close to the actual MATLAB language as possible, Octave may be a good fit for you; it strives for exact compatibility, so many of your...
Source: www.openhealthnews.com

machine-learning in Python Reviews

We have no reviews of machine-learning in Python yet.
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Social recommendations and mentions

Based on our record, machine-learning in Python should be more popular than GNU Octave. It has been mentiond 7 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.

GNU Octave mentions (1)

  • everyday I get more certain that Algerian universities sucks...
    As for Matlab, I think you'll be just fine with using GNU Octave. Source: about 3 years ago

machine-learning in Python mentions (7)

  • Data science and cybersecurity with python project
    After that you should probably look at some very basic ML tutorials. I just googled it, I have no idea if this is good https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: about 2 years ago
  • Ask HN: How can I learn ML in 6 months as a teenager?
    Few different approaches based on search engine 'ml with python': Work though use cases / examples : https://www.databricks.com/resources/ebook/big-book-of-machine-learning-use-cases On-line class(es) / step by step projects: * https://bootcamp-sl.discover.online.purdue.edu/ai-machine-learning-certification-course * https://www.w3schools.com/python/python_ml_getting_started.asp *... - Source: Hacker News / over 2 years ago
  • Are these CS courses enough CS knowledge for ML engineer?
    MLE: ALL OF THE ABOVE (this is important - pure machine learning skills generally won’t make you hireable unless you’re doing a PhD and/or are a genius) Plus: 1. https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ 2. https://www.coursera.org/learn/machine-learning 3. https://www.3blue1brown.com/topics/neural-networks. Source: about 3 years ago
  • how to do i train an AI
    Have you seen this? https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 3 years ago
  • Python Data Science Project Ideas (+References)
    Machine learning models Fine-tune existing machine learning models for improved accuracy, or create your own custom models. - Source: dev.to / over 3 years ago
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What are some alternatives?

When comparing GNU Octave and machine-learning in Python, you can also consider the following products

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Wolfram Mathematica - Mathematica has characterized the cutting edge in specialized processing—and gave the chief calculation environment to a large number of pioneers, instructors, understudies, and others around the globe.

BigML - BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.

Scilab - Scilab Official Website. Enter your search in the box aboveAbout ScilabScilab is free and open source software for numerical . Thanks for downloading Scilab!

Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.

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