awesome-datascience VS TFLearn

Compare awesome-datascience vs TFLearn and see what are their differences.

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awesome-datascience TFLearn
9 2
23,777 9,606
3.7% 0.0%
6.9 0.0
5 days ago 5 months ago
Python
MIT License GNU General Public License v3.0 or later
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.

awesome-datascience

Posts with mentions or reviews of awesome-datascience. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-26.

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.

What are some alternatives?

When comparing awesome-datascience and TFLearn you can also consider the following projects:

Awesome-VAEs - A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

Keras - Deep Learning for humans

gdelt

tensorflow - An Open Source Machine Learning Framework for Everyone

vagas-junior-estagio - Empresas que constantemente oferecem vagas para junior e estagiários sem exigir experiência prévia

scikit-learn - scikit-learn: machine learning in Python

DataScienceResources - Open Source Data Science Resources.

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

data-science-blogs - A curated list of data science blogs

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.

ScribeSalad - A collection of YouTube videos transcripts : Podcasts (Joe Rogan Experience, Tim Ferris, Jocko podcast, ..), lectures (YaleCourses, MIT lectures, Jordan B. Peterson talks, ..). A big transcripts salad spanning history, geography, science, politics, film making and more.

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