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TFlearn VS MLKit

Compare TFlearn VS MLKit and see what are their differences

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LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

TFlearn logo TFlearn

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

MLKit logo MLKit

MLKit is a simple machine learning framework written in Swift.
Not present
  • MLKit Landing page
    Landing page //
    2023-09-15

TFlearn features and specs

  • User-Friendly Interface
    TFlearn provides a higher-level API that simplifies the process of building and training deep learning models, making it easier for beginners to use TensorFlow.
  • Modular Design
    It offers modular abstraction layers, allowing users to construct neural networks using pre-defined blocks which are easy to stack and customize.
  • Integration with TensorFlow
    TFlearn is built on top of TensorFlow, providing the flexibility and performance benefits of TensorFlow while enhancing its usability.
  • Pre-built Models
    It includes a range of pre-built models and algorithms for common machine learning tasks like classification and regression, facilitating quick experimentation.

Possible disadvantages of TFlearn

  • Lack of Updates
    TFlearn has not been actively maintained or updated in recent years, which may lead to compatibility issues with the latest versions of TensorFlow.
  • Limited Flexibility
    While TFlearn offers a simplified API, it may not offer the same level of customization and flexibility as using TensorFlow's core API directly.
  • Smaller Community
    As a niche library, TFlearn has a smaller user community, which could result in less community support and fewer resources compared to more popular libraries like Keras.
  • Performance Limitations
    Though built on top of TensorFlow, the added abstraction layers in TFlearn could potentially lead to minor performance overhead compared to pure TensorFlow implementations.

MLKit features and specs

  • Feature-Rich
    MLKit offers a wide range of functionalities including text recognition, barcode scanning, image labeling, and face detection, making it a robust choice for various machine learning tasks.
  • Ease of Integration
    The library is designed with a user-friendly API that simplifies the integration of machine learning capabilities into Android applications.
  • Regular Updates
    Frequent updates ensure that the library stays current with the latest advancements in technology and addresses any vulnerabilities or performance issues.
  • Open-Source
    Being open-source allows developers to contribute to and modify the library as needed, fostering a community of collaboration and improvement.

Possible disadvantages of MLKit

  • Platform Limitation
    MLKit is tailored specifically for Android, which may limit its applicability if cross-platform compatibility is required.
  • Documentation
    Although the library is feature-rich, some users have reported that the documentation could be more comprehensive, which might hinder new users.
  • Performance Overhead
    Integrating advanced features may lead to increased resource consumption, potentially affecting the performance of the host application.
  • Community Size
    Compared to more established machine learning frameworks, MLKit has a relatively smaller user base, which can impact the volume of community support and shared resources.

TFlearn videos

+ Add

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

MLKit videos

+ Add

Android Face Detection using Camera - Google MLKit Face Detection Android Studio - Firebase ML Kit

Category Popularity

0-100% (relative to TFlearn and MLKit)

TFlearn

MLKit

Data Science And Machine Learning
45 45%
Data Science And Machine Learning
55% 55
OCR
100 100%
OCR
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0

User comments

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Social recommendations and mentions

Based on our record, TFlearn seems to be more popular. 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.

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 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 4 years ago

MLKit mentions (0)

We have not tracked any mentions of MLKit yet. Tracking of MLKit recommendations started around Mar 2021.

What are some alternatives?

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

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

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

Clarifai - The World's AI

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

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.

OpenCV - OpenCV is the world's biggest computer vision library

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