Overview
What is Keras?
Keras is a Python deep learning library
Keras, the right entry to the Deep Learning world
Keras Review
Rapidly build neural network
Best wrapper library for TensorFlow and Theano
Best wrapper library for TensorFlow
Best wrapper library for TensorFlow and Theano.
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What is Keras?
Keras Technical Details
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(18)Community Insights
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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 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:
- 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.
- Lack of Pre-trained Models: Several reviewers have mentioned that Keras lacks pre-defined trained models, which are available in other deep learning libraries.
- 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.