DeepPy

(5)
4.7 out of 5 stars

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 based on NumPy's ndarray,has a small and easily extensible codebase, runs on CPU or Nvidia GPUs and implements the following network architectures feedforward networks, convnets, siamese networks and autoencoders.

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DeepPy review by Rayan V.
Rayan V.
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"A Python Deep Learning Library "

What do you like best?

- Easy to install and use.

- Support for basic python libraries which are very much helpful in Data Science like NumPy.

What do you dislike?

- Documentation can be improved.

- No proper explanations provided for using API, becomes difficult for a learner.

Recommendations to others considering the product

There is an example section provided at the main web page of DeepPy: https://andersbll.github.io/deeppy-website/ , implement one or two then try to make a simple model. It will really help to get control over DeepPy.

What business problems are you solving with the product? What benefits have you realized?

- Have built a whole image recognition project totally based on DeepPy at core.

- Also implemented some basic projects based on Deep Learning.

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DeepPy review by Jaykishan B.
Jaykishan B.
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"Deep Learning with Python"

What do you like best?

- Support Feedforward networks and Convnets, really makes most of your task easy.

- There is a whole example of Image Classification with Convnets, helped a lot to build my own.

What do you dislike?

- Very helpful library but lacks in proper documentation and tutorials.

Recommendations to others considering the product

Checkout this Image Classification example using DeepPy: https://andersbll.github.io/deeppy-website/examples/convnet_mnist.html , before you build your own.

What business problems are you solving with the product? What benefits have you realized?

- Build many models based on Feedforward networks and Convnets.

- Developed Image Classification and Recognition model from scratch and just using DeepPy.

What Image Recognition solution do you use?

Thanks for letting us know!
DeepPy review by Tejasvini V.
Tejasvini V.
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Verified Current User
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"DeepPy - A Deep Learning library in python"

What do you like best?

- Implementation of Deep Learning algorithms becomes very easy with DeepPy.

- It also supports Convnets, Siamese networks and Autoencoders.

What do you dislike?

Documentation is not that well. It becomes difficult for a beginner developer to understand.

Recommendations to others considering the product

Many examples are given in the documentation: https://andersbll.github.io/deeppy-website/examples/index.html , try them to learn the basics before you start developing your own models.

What business problems are you solving with the product? What benefits have you realized?

We have developed Deep Learning (mainly image classification) models using DeepPy which were later used in many applications and software.

DeepPy review by Rahul T.
Rahul T.
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"Deeply implemented Deep Learning library for python"

What do you like best?

- Support for NumPy ndarrays.

- The example section: http://andersbll.github.io/deeppy-website/examples/index.html first and foremost place to get handy with DeepPy

- Great support for already implemented network architectures like feedforward networks, convnets and siamese networks.

What do you dislike?

- Documentation is not enough for new programmer in Deep Learning.

- No sufficient tutorials available.

Recommendations to others considering the product

Start with example section: http://andersbll.github.io/deeppy-website/examples/index.html if you are new to Deep Learning or python.

What business problems are you solving with the product? What benefits have you realized?

Developing products based on Deep Learning algorithms and image recognition.

DeepPy review by Geoffrey F.
Geoffrey F.
Validated Reviewer
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"Efficient learning"

What do you like best?

ease of implementation, it took very little effort to get it up and running

What do you dislike?

rigidity of design makes optimization difficult

What business problems are you solving with the product? What benefits have you realized?

NLP, information extraction, proper classification of semantic relationships

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