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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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What is Keras?
Keras is a Python deep learning library
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What is Keras?
Keras is a Python deep learning library
Keras Technical Details
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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.
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November 10, 2020
Keras Review
Score 9 out of 10
Vetted Review
Verified User
My general experience is positive. It may give some new software engineers a marginally misshaped Idea of how things work - since it is genuinely simple to building an amazing neural network with it, yet it could likewise urge them to burrow further. Building even a basic NN with C without any preparation would disappoint most fledglings, so this is a decent spot for understudies to begin - accepting that they're likewise examining hypothesis.
- Until we have IDEs that can make an interpretation of our idea into code, I don't think making Deep Learning models could be made a lot simpler.
- It's makes the process easy for building the Neural Network.
- Doesn't require to have strong background in Deep Learning.
- I didn't face any issue so far.
- The only thing, you can't modify everything in this. So it's not recommended for constructing highly optimised algorithms.
- Really good for beginners.
- Easy to use.
- Strong community and customer support.
For beginners, I always recommend starting with Keras, because it's really easy to use and learn at first. There is not much pre-requisite for this to start with.
November 09, 2020
Rapidly build neural network
Keras isn't utilized over the entire association, however it is being utilized by a portion of our specialties. In those offices, the vast majority of them are utilizing it to do some sort of AI task, which fundamentally incorporates planning and actualizing the neural organization. I have utilized this for loads of reasons. Every one of them was in AI fields, similar to picture preparing, essential grouping, and considerably more.
- Easy to use. We can implement neural networks easily.
- There is a lot of built-in utility that makes the task easier.
- It also supports TensorFlow.
- We can't modify everything that we want to.
- Some built-in model can be included as a part of this library.
- Resource requirement is quite high for using this library.
- Easy and faster way to develop neural network.
- It would be much better if it is available in Java.
- It doesn't allow you to modify the internal things.
Keras is much easier to learn as compared to TensorFlow. It also has a lot of built-in functionality that makes it much better than the alternatives.