
Keras
Clarifai
DeepPy
Microsoft Cognitive Toolkit (Formerly CNTK)
Merlin
Knet
Swift Brain
TFlearn is a modular and transparent deep learning library built on top of Tensorflow.

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Which is more popular?
AllCode might be a bit more popular than TFlearn. We know about 2 links to it since March 2021 and only 2 links to TFlearn.
Website, pricing, platforms and company facts side by side.
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| Website | tflearn.org | allcode.com |
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Face Recognition using Deep Learning | Convolutional-Neural-Network | TensorFlow | TfLearn
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TFLearn – Deep learning library featuring a higher-level API for TensorFlow. - Source: dev.to / about 4 years ago
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... - Source: dev.to / over 5 years ago
Software development services is the process of creating and maintaining the various components of software, including applications and frameworks. Source: almost 4 years ago
Looking for hiring thefront and backend developer? Contact with Allcode and get the best full stack developers at best price in the USA. For more details contact us now. Source: almost 5 years ago
When comparing TFlearn and AllCode, you can also consider the following products.

Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Compare Keras to TFlearn or AllCode:


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.
Compare DeepPy to TFlearn or AllCode:

Machine Learning
Compare Microsoft Cognitive Toolkit (Formerly CNTK) to TFlearn or AllCode:

Merlin is a deep learning framework written in Julia, it aims to provide a fast, flexible and compact deep learning library for machine learning.
Compare Merlin to TFlearn or AllCode:

Knet is a deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models.
Compare Knet to TFlearn or AllCode: