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XGBoost VS TFlearn

Compare XGBoost VS TFlearn and see what are their differences

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XGBoost logo XGBoost

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.

TFlearn logo TFlearn

TFlearn is a modular and transparent deep learning library built on top of Tensorflow.
  • XGBoost Landing page
    Landing page //
    2023-07-30
Not present

XGBoost videos

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XGBoost Part 3: Mathematical Details

More videos:

  • Review - XGBoost A Scalable Tree Boosting System June 02, 2016
  • Review - Free Udemy Course - CatBoost vs XGBoost - Classification and Regression Modeling with Python

TFlearn videos

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

Category Popularity

0-100% (relative to XGBoost and TFlearn)

XGBoost

TFlearn

Data Science And Machine Learning
37 37%
Data Science And Machine Learning
63% 63
OCR
0 0%
OCR
100% 100
Business & Commerce
100 100%
Business & Commerce
0% 0
Online Services
100 100%
Online Services
0% 0

User comments

Share your experience with using XGBoost 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 XGBoost. 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.

XGBoost mentions (1)

  • CS Internship Questions
    By the way, most of the time XGBoost works just as well for projects, would not recommend applying deep learning to every single problem you come across, it's something Stanford CS really likes to showcase when it's well known (1) that sometimes "smaller"/less complex models can perform just as well or have their own interpretive advantages and (2) it is well known within ML and DS communities that deep learning... Source: almost 2 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 / over 1 year 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 XGBoost and TFlearn, you can also consider the following products

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

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

Open Text Magellan - OpenText Magellan - the power of AI in a pre-wired platform that augments decision making and accelerates your business. Learn more.

Clarifai - The World's AI

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

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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