TFLearn VS scikit-learn

Compare TFLearn vs scikit-learn and see what are their differences.

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TFLearn scikit-learn
2 85
9,618 59,980
0.0% 0.7%
0.0 9.9
6 months ago 7 days ago
Python Python
GNU General Public License v3.0 or later 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.

TFLearn

Posts with mentions or reviews of TFLearn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-14.
  • Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
    8 projects | dev.to | 14 Aug 2022
    TFLearn – Deep learning library featuring a higher-level API for TensorFlow
  • Base ball
    1 project | dev.to | 20 Mar 2021
    Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBI’s, and walk’s are all taken into account and passed through layers. There’s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called TFlearn, documentation available from http://tflearn.org. The program will output the home and away teams as well as their respective score predictions.

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 2024-11-05.
  • State of Python 3.13 Performance: Free-Threading
    5 projects | news.ycombinator.com | 5 Nov 2024
    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.

  • GitHub Repositories Every Developer Should Know: An In-Depth Guide
    20 projects | dev.to | 24 Oct 2024
    Visit the repository and explore examples.
  • Essential Deep Learning Checklist: Best Practices Unveiled
    20 projects | dev.to | 17 Jun 2024
    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.
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    1 project | dev.to | 5 May 2024
    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!
  • AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
    8 projects | news.ycombinator.com | 9 Apr 2024
    Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

    - From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

    - Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

    There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

  • Polars
    11 projects | news.ycombinator.com | 8 Jan 2024
    sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.
  • [D] Major bug in Scikit-Learn's implementation of F-1 score
    2 projects | /r/MachineLearning | 8 Dec 2023
    Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!
  • Contraction Clustering (RASTER): A fast clustering algorithm
    1 project | news.ycombinator.com | 27 Nov 2023
  • Ask HN: Learning new coding patterns – how to start?
    3 projects | news.ycombinator.com | 10 Nov 2023
    I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.

    I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.

    You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.

    As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?

  • Transformers as Support Vector Machines
    1 project | news.ycombinator.com | 3 Sep 2023
    It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says

    Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

    [0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...

    [1] https://en.wikipedia.org/wiki/Platt_scaling

    [2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...

What are some alternatives?

When comparing TFLearn and scikit-learn you can also consider the following projects:

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.

tensorflow - An Open Source Machine Learning Framework for Everyone

Surprise - A Python scikit for building and analyzing recommender systems

skflow - Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

NuPIC - Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.

tfgraphviz - A visualization tool to show a TensorFlow's graph like TensorBoard

gensim - Topic Modelling for Humans

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

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.

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