Name: Keras
Rating: 7 (19 reviews)
Author: Open Source

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### Deep Learning Techniques
User sentiment regarding Keras's deep learning techniques is predominantly positive, with many praising its ease of use and quick implementation for building various deep neural networks. The flexibility of Keras to run on top of different frameworks like TensorFlow, CNTK, and Theano is appreciated, along with its modular and user-friendly design. Users find Keras particularly beneficial for beginners in deep learning, as it allows for the development of complex models with minimal lines of code. Additionally, the availability of pre-trained models and GPU support further enhances the deep learning experience with Keras. However, some users note limitations in customization for more advanced deep learning models, suggesting a preference for direct TensorFlow coding in such cases.

### Neural Network Architectures
Users generally believe that Keras's neural network architectures functionality is a standout feature, with many praising its ease of use and flexibility in building various types of neural networks. The integration with TensorFlow and the availability of pre-trained models have been highlighted as significant advantages. Additionally, the modular and user-friendly nature of Keras's neural network architecture design has garnered positive feedback, making it accessible even for beginners in the field of deep learning. The ability to run on top of multiple frameworks and leverage GPU acceleration for faster computations has also been well-received by users.

### Machine Learning Frameworks
Users generally agree that Keras's machine learning framework capabilities are a strong suit. The framework's ease of use, integration with TensorFlow, and availability of pre-trained models are highlighted as key advantages. However, some users express concerns about the framework's high-level nature, which can limit customization options and make certain tasks more challenging. Despite these drawbacks, Keras remains a popular choice for beginners and experienced practitioners alike due to its user-friendly interface and efficient neural network development features.

### Model Training and Implementation
Users generally appreciate Keras for its ease of building and training deep learning models. The consensus seems to be that Keras provides a user-friendly interface for creating and implementing various types of models, making it accessible even to beginners. While some users find the model training process straightforward and efficient, others express concerns about the compatibility with certain systems and the limitations in customizing complex architectures. Despite these drawbacks, Keras remains a popular choice for those looking to quickly prototype and deploy deep learning models.

### TensorFlow Integration
User sentiment regarding Keras's TensorFlow integration varies widely. While some users appreciate the ease of implementation and the high-level abstraction it provides, others express concerns about its compatibility with TensorFlow and the need for more customization options. The integration with TensorFlow backend seems to be a point of contention among users, with some finding it beneficial for quick model building, while others feel it lacks certain functionalities and updates. The overall consensus suggests that while Keras offers a user-friendly framework, opinions on its TensorFlow integration remain mixed.

### GPU Acceleration and Performance
User feedback on Keras's GPU acceleration and performance features is varied and covers a range of opinions. While some users appreciate the ease of running models smoothly on both GPU and CPU, others find challenges in transferring models between multiple GPUs. The compatibility with TensorFlow for GPU usage is highlighted as a positive aspect, along with the support for multi-GPU and distributed training. However, concerns are raised about the complexity of running the Keras library on GPU compared to CPU and the large memory consumption of pre-trained models. Despite mixed opinions, the general sentiment leans towards acknowledging the benefits of GPU acceleration in enhancing model training efficiency within the Keras framework.

### Image and Object Detection
Users generally praise Keras for its robust image and object detection capabilities. The consensus among reviewers is that Keras excels in tasks such as image classification, object tracking, and object detection. Many users appreciate the ease of integrating pre-trained models like ResNet and VGG19 for efficient image processing. Keras's support for various image preprocessing functions and its compatibility with different architectures make it a popular choice for developing accurate and reliable object detection models. Additionally, users highlight the smooth performance of Keras on both GPU and CPU, emphasizing its efficiency in handling complex image analysis tasks.

### Project Documentation and Examples
User opinions vary when it comes to Keras's documentation and examples resources. While some users praise the comprehensive documentation and abundance of code examples, others express frustration over the lack of in-depth explanations for advanced features and concepts. The availability of pre-trained models and the ease of building complex models with minimal code are highlighted as strengths, but the need for more detailed documentation on certain obscure options remains a concern for some users. Despite mixed feedback, the active Keras community and user-friendly aspects are acknowledged as valuable resources for beginners navigating the project documentation and examples.

### Customization and Prototyping
While some users appreciate the ease of prototyping with Keras, there is a prevailing sentiment that the platform falls short in terms of advanced customization options. Users have expressed frustration with the limitations in tweaking and customizing models to meet specific requirements. The consensus seems to be that while Keras offers a user-friendly interface for quick prototyping, it lacks the depth of customization needed for more complex model modifications.

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.

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.

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.

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Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.

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Keras 2020-11-06 01:04:30

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Keras 2020-11-04 03:39:50

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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.

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.

TensorFlow, Caffe Deep Learning Framework, Amazon Deep Learning AMIs

Keras is not used across the whole organization, but it is being used by some of our departments only. And in those departments, most of them are using it to do some kind of machine learning task, which basically includes designing and implementing the neural network. I have used this for lots of reasons. All of them were of machine learning fields, like image processing, basic classification, and much more.

Performs well when you are doing some implementation which requires neural network implementation and some other deep learning models
It has lots of inbuilt tools which you can have clean your data before processing
It supports Tensorflow as its backend, so it can easily use GPU

As it is a kind of wrapper library it won't allow you to modify everything of its backend
Unlike other deep learning libraries, it lacks a pre-defined trained model to use
Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs

It helped me in learning the basic concept of deep learning by having hands-on experience.
It has helped us to implement our NN with very little time.
It doesn't give you the whole power to customize your neural network. If you want that then you have to shift to TensorFLow

TensorFlow, Caffe Deep Learning Framework and Theano

Keras is being used during hackathon in my current company. And it's not used by across the company. Basically, during hackathon lots of people are working on machine learning projects that includes deep learning as well. So, there are lots of people who are using Keras for neural network implementation. And I have used this in my during my college and in company as well. We have used Keras to implement neural network for image recognition and in other things as well.

One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
It also provides functionality to develop models on mobile device.

As Keras works at a high level of abstraction, it limits the user to use it's own implemented algorithm. It doesn't give complete power to user to modify or implementing their own basic algorithm.
Sometimes it is slow on GPU as compared to the pure tensorflow.
Other than the above two cons, I don't think it has any negatives.

It made our development faster and easy as well.
Sometime, when we need to change the basic algorithm, when we need to configure the neural network configuration then it doesn't allow us to modify that.
As it comes with lot of inbuilt features of data processing, it is easy to process the data.

TensorFlow and Caffe Deep Learning Framework

TensorFlow, Theano, Caffe Deep Learning Framework

Keras is being used to develop data science models for predictions that include implementing neural networks and others as well. It is not being used by all of us in our company but only by the data science team. We have used this not only for prediction, but for building NLP models as well. We have used this to implement LSTM. Basically, we use this to understand the natural language and to process that.

Implementing neural networks and deep learning models is easy with this.
Data processing is easy with Python and Keras. Keras helps a lot and has a good collection of functions to do data processing.
It has good integration with other devices like Android.

With Keras you don't have much power to configure your model. So, if it can be possible to do the customization to the deep level, then it will be good.
It is only available for Python, doesn't have other language support.
Would love to see dynamic chart creation, like PyTorch

Good and easy way to develop neural network models
Doesn't provide support for language other than Python
Developed a natural language processing model that is quite easy and efficient

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Keras

Overview

What is Keras?