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AutoGluon VS TFlearn

Compare AutoGluon VS TFlearn and see what are their differences

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AutoGluon logo AutoGluon

Application and Data, Application Utilities, and Machine Learning Tools

TFlearn logo TFlearn

TFlearn is a modular and transparent deep learning library built on top of Tensorflow.
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AutoGluon videos

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AutoML using AutoGluon

More videos:

  • Review - AutoGluon Overview ICML'20 Workshop
  • Tutorial - CVPR Tutorial: Introducing AutoGluon in 20 minutes

TFlearn videos

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Face Recognition using Deep Learning | Convolutional-Neural-Network | TensorFlow | TfLearn

Category Popularity

0-100% (relative to AutoGluon and TFlearn)

AutoGluon

TFlearn

Data Science And Machine Learning
27 27%
Data Science And Machine Learning
73% 73
OCR
0 0%
OCR
100% 100
AI
100 100%
AI
0% 0
Machine Learning
27 27%
Machine Learning
73% 73

User comments

Share your experience with using AutoGluon and TFlearn. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, TFlearn should be more popular than AutoGluon. It has been mentiond 2 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.

AutoGluon mentions (1)

  • Hyperparameter Optimization (HPO) using AutoGluon
    Hey Folks - I recently learned about AutoGluon (https://auto.gluon.ai) and was hoping to use it for HPO among other ML tasks! Using their quick quid, I can successfully use their TabularPredictor for my regression problem and get a number of models trained and have access to a number of details, e.g., performance, and hyperparameters used. However, using the same dataset I fail (with somewhat of a cryptic error... Source: almost 3 years ago

TFlearn mentions (2)

  • Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
    TFLearn – Deep learning library featuring a higher-level API for TensorFlow. - Source: dev.to / almost 2 years ago
  • Base ball
    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... - Source: dev.to / about 3 years ago

What are some alternatives?

When comparing AutoGluon and TFlearn, you can also consider the following products

R Caret - Documentation for the caret package.

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

H2O.ai - Democratizing Generative AI. Own your models: generative and predictive. We bring both super powers together with h2oGPT.

Clarifai - The World's AI

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

DeepPy - DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming.

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