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Keras

Keras

Overview

What is Keras?

Keras is a Python deep learning library

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Keras, a popular machine learning library, has been utilized by multiple individuals in my company for various use cases. Specifically, it …
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Keras Review

9 out of 10
November 10, 2020
My general experience is positive. It may give some new software engineers a marginally misshaped Idea of how things work - since it is …
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What is Keras?

Keras is a Python deep learning library

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Product Details

What is Keras?

Keras is a Python deep learning library

Keras Technical Details

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Reviews and Ratings

(18)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Keras, a popular machine learning library, has been utilized by multiple individuals in my company for various use cases. Specifically, it has been successfully employed during hackathons and machine learning projects. While not used company-wide, Keras is favored by specific departments like the data science team.

One of the main use cases of Keras is implementing neural networks, particularly for image recognition and other machine learning tasks. It has proven effective for applications such as image processing and basic classification. Additionally, Keras has been used to develop data science models, including neural networks and NLP models like LSTM. Users have found Keras to be a great starting point for beginners in machine learning, as it simplifies the process of building neural networks.

Keras is often integrated with TensorFlow in the Data & AIML department, where it serves as a high-level API for model designing, training, evaluation, and inference/prediction. It plays a crucial role in the production environment by incorporating into a final Model As Service product. This allows various business applications to consume its prediction output for making important decisions. Overall, Keras has demonstrated its value in diverse machine learning applications within our organization.

Easy to use: Many users have found Keras to be easy to use, especially when implementing neural networks and deep learning models. They appreciate that with just one line of code, they can add a layer to the neural network with all its configurations.

Wide range of built-in features: Users appreciate that Keras provides a wide range of built-in features such as cov2d, conv2D, and maxPooling layers. This allows for fast development without the need to write everything from scratch.

Convenient mobile implementation: Several reviewers have mentioned that they find it convenient that Keras offers functionality to develop models on mobile devices. This is particularly helpful for users who require mobile implementation.

Cons:

  1. Limited Customization Options: Some users have expressed that Keras does not provide much power for configuring models, limiting their ability to modify or implement their own basic algorithms as it works at a high level of abstraction.
  2. Lack of Pre-trained Models: Several reviewers have mentioned that Keras lacks pre-defined trained models, which are available in other deep learning libraries.
  3. Documentation and Error Handling: Users have found the errors thrown by Keras to be unhelpful for debugging, making it difficult to determine the root cause of issues. Additionally, documentation on advanced topics like distributed training is not clear and lacks real code examples according to some users.

Users often recommend Keras as a great starting point for beginners and small scale projects. They advise newcomers to begin with Keras to gain a better understanding of deep learning, while also emphasizing the importance of reading the documentation and exploring the provided examples. Users suggest that it's helpful to have a foundational knowledge of basic machine learning concepts before diving into Keras. Additionally, Keras is highly recommended for quickly prototyping new ideas and gaining insights into neural networks, although it may not be suitable for more complex models.

Attribute Ratings

Reviews

(1-6 of 6)
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Rounak Jangir | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Keras is a good point where you can learn lots of things and also have hands-on experience. There is not much comparison of Keras with Tensorlow, as Keras is a wrapper library which supports TensorFlow and Theano as backends for computation. But once you have enough knowledge of deep learning and machine learning, then it's better if you use TensorFlow itself or some other library.
Raghuvar Prajapati | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.
Gaurav Yadav | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
TensorFlow and Caffe are bit hard to learn but they give you power to implement everything by you own. But most of the time it is not required to implement our own algorithm, we can solve the problem with just using the already provided algorithms. As compared to TensorFlow and Caffe, Keras is very easy to use and develop things.
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