Software Alternatives, Accelerators & Startups

Thinc VS TFlearn

Compare Thinc VS TFlearn and see what are their differences

Thinc logo Thinc

Thinc is a lightweight type-checked deep learning library for composing models, with support for layers defined in frameworks like PyTorch and TensorFlow.

TFlearn logo TFlearn

TFlearn is a modular and transparent deep learning library built on top of Tensorflow.
  • Thinc Landing page
    Landing page //
    2023-05-14
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Thinc videos

Best Practices for Fingerprint Enrolment Process on ThinC-AUTH Biometric Security Key

More videos:

  • Review - HOOMAN.. DON EVEN THINC ABOUT IT
  • Review - Review jujur : somethinc aha bha pha peeling solution the ordinary aha bha peeling solution

TFlearn videos

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

Category Popularity

0-100% (relative to Thinc and TFlearn)
Data Science Tools
100 100%
0% 0
OCR
0 0%
100% 100
Data Science And Machine Learning
AI
100 100%
0% 0

User comments

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Social recommendations and mentions

Based on our record, TFlearn should be more popular than Thinc. 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.

Thinc mentions (1)

  • good examples of functional-like python code that one can study?
    Thinc - defining neural nets in functional way Jax, a new deep learning framework puts emphasis on functions rather than tensors, I've tested it for a couple of applications and it's really cool, you can write stuff like you'd write math expressions in papers using numpy. That speeds up development significantly, and makes code much more readable. 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 Thinc and TFlearn, you can also consider the following products

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

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

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

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.