Software Alternatives & Reviews

Neurolab VS DeepPy

Compare Neurolab VS DeepPy and see what are their differences

Neurolab logo Neurolab

Neurolab is a simple and powerful Neural Network Library for Python that contains based neural networks, train algorithms and flexible framework to create and explore other neural network types.

DeepPy logo 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.
  • Neurolab Landing page
    Landing page //
    2023-10-10
  • DeepPy Landing page
    Landing page //
    2019-06-12

Neurolab

Categories
  • OCR
  • Machine Learning
  • Image Analysis
  • Deep Learning
Website github.com

DeepPy

Categories
  • OCR
  • Machine Learning
  • Image Analysis
  • Data Science And Machine Learning
Website andersbll.github.io

Neurolab videos

NeuroLab Urine & Saliva Test Instructional Video

More videos:

  • Review - Alzheimer's Disease_Ruffin NeuroLab RIP 20200909 Malcolm Lee I
  • Review - N.PHONE Smart Hud (Next.Gen AIO HUD Technology) (EN/FR) Neurolab Inc. (Second Life)

DeepPy videos

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Category Popularity

0-100% (relative to Neurolab and DeepPy)
OCR
16 16%
84% 84
Image Analysis
23 23%
77% 77
Machine Learning
18 18%
82% 82
Data Science And Machine Learning

User comments

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What are some alternatives?

When comparing Neurolab and DeepPy, 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.

Clarifai - The World's AI

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

Microsoft Cognitive Toolkit (Formerly CNTK) - Machine Learning

Merlin - Merlin is a deep learning framework written in Julia, it aims to provide a fast, flexible and compact deep learning library for machine learning.

Knet - Knet is a deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models.