Keras Reviews

What you need definitely to start your deep learning experiments

Comments: I would defintely recommend it as the quickest step to start testing your model.

Pros:

Keras is the only platform that runs on top of most popular backends like TensorFlow, pyTorch and Microsoft Cogntitive Toolkit. This gives great flexibility to researchers to try their network architecture with minimal changes across multiple libraries mentioned. The sequencing modularity is what makes you build sophisticated network with improved code readability .

Cons:

If you encounter an error, it is hard to be debugged.

Keras for deep learning

Comments: I did many deep learning projects using keras it is really helpful

Pros:

easy to use, large communities and support

Cons:

keras has many predefined methods and functions but it is difficult to integrate a custom class.

Keras for school project

Pros:

I did use this library couple of times during the semester to solve my deep learning course home works and project. compared to tensor flow it was easier for me to use

Cons:

It was not still easy to use and well documented with examples

Great Deeplearning framework

Comments: i use keras for image classification making use of it's pretrained architectures especially the resnet architectures.

Pros:

What i love most about keras is it's wrapper functions, i use it to perform Gridsearch using scikitlearn and this is amazing as i cannot do this on other frameworks. keras also has a good documentation page with lots of pretrained CNN architectures for image classifications solutions.

Cons:

Nothing to dislike about this framework yet.

Start Learning From Keras Framework

Comments: I recommend it for performing image classification as it provides some inbuilt fucntionality for image preprocessing. It even comes with many usefull pre-trained models like resnet.

Pros:

First thing i like about Keras is that it runs on the top of tensorflow background. Deep learning and neural network construction and visulaization is simple using Keras, also it comes with enough documentations. It provides lots of inbuilt functions for image processing which makes it lots easier for image classificaiton.

Cons:

For building more customized deep learning model, you need to use TensorFlow. Also the model inferencing time is little slow compared to model directly build in TensorFlow.

Start Here

Comments: My overall experience is positive. It might give some newbie programmers a slightly distorted idea of how things work - since it is fairly easy to building powerful neural networks with it, but it could also encourage them to dig deeper. Building even a simple NN with C from scratch would frustrate most beginners, so this is a good place for students to start - assuming they're also studying theory.

Pros:

Until we have IDEs that can translate our thoughts into code, I don't think creating Deep Learning models could be made much easier. Keras doesn't ask a lot of the user in terms of background knowledge or coding skill, so it's your best bet for rapidly building applications that require some artificial intelligence. Yes, you should have some basic familiarity with what's going on under the hood, but you don't need to memorize a neural networks textbook.

Cons:

As I go on using it I suspect its limitations will become more apparent. On the other hand, that's not really an issue since it can be easily extended. It plays nicely with TensorFlow in my experience, but I haven't seen how well it works with PyToch or Microsoft's cognitive toolkit.

keras - an easy way to develop machine learning models

Pros:

It has made machine learning and deep learning implementation very easy as compared to tensorflow. Implementing deep learning models using tensorflow is very difficult, you have to take care of each and every variables but if you are using keras it's very easy to do this. With just few lines of code you can develop a deep learning model. Keras also provide lots of functionality for data processing like converting to one hot encoding and lot other.

Cons:

As it provides lots of easy way to implement algorithm but it restricts you to use those functionality only. If you want to build good algorithm with lot of optimization, you can't do everything with keras.

Keras is the best API and framework for deep learning application development

Comments: I have developed many deep learning applications using keras.

Pros:

Many ready available function are written by community for keras for developing deep learning applications. It is easy to use and user friendly.

Cons:

Backend support is available only with theano or tensorflow.

Best wrapper library for tensorflow an theano -- very easy to use

Comments: have made writing neural network implementation very easy

Pros:

While writing the neural network with tensorflow, we need to take care of every thing like input layer size, output layer size, bias vector size. We have to design the whole layer itself. But with this library, it can be done in just one line. Also it has lots of inbuilt feature for data processing which makes it very usable. And it's support for both tensorflow and theano, makes it more advance.

Cons:

It is best wrapper library over tensorflow, but it restrict you to use their implemented algorithm. Although, you can configure the inbuilt functionality, but then it would be better to do that with tensorflow only.

Keras

Pros:

A high level framework built on Tensorflow, makes writing deep learning codes fun

Cons:

It automatically loads all the dataset to ram, meaning you need have sufficient computational capacity

Keras: A High-level API for Machine Learning Applications

Comments: Great experience using Keras to do high-level ML development without going into the low-level backend.

Pros:

I enjoyed the simplified Python API provided by Keras to manage the different aspects of Machine Learning training and Data Set preparation. I used it to implement convolutional neural network models for image/video recognition for detecting the psychological state of a human entity using the facial expressions. Keras supported a very simplified interface for implementing the different aspects of the ML application. Moreover, it demonstrated very easy model to save the training stages of the ML model and even to migrate it to other servers. I would definitely rely on Keras for high-level ML applications without going into the thorny TensorFlow API.

Cons:

The main issue I had in Keras is figuring out some low-level error messages that seemed cryptic to me at start. Perhaps this is not Keras fault as it is designed to be a simplified high-level API to abstract the knotty details of ML. But still some documentation to support this would be highly appreciated.

My Review of Keras

Comments: My overall experience with Keras is quite good as it provides a variety of built-in functions.

Pros:

I like that Keras can be used in servals areas as it combines a lot of built-in functions. I love the documentations that Keras provides for beginners and the community of Keras is very large and supportive. Also, It is open-source and provides different neural network models.

Cons:

It is a little bit too hard to run Keras library on GPU instead of CPU in order to enhance the model training and reduce the time. Also, I don't like the large size of the pre-trained models that I get from Keras as they consume a lot of memory.

A great library for training Deep Neural Networks

Comments: Keras is fully compatible with Core ML - this allows our dev team to build complex mobile applications on the latest iOS devices.

Pros:

Python is easy to use and extensible. The modularity of these libraries is the future of building complex machine learning models. Keras is one of the better frameworks out there right now. It allows us to train deep neural nets at a reasonable rate. Keras is compatible with Apple's Core ML which is very useful for our moblie app development.

Cons:

Keras is a little limited in what it can handle. Luckily there are other frameworks popping up every day to supplement any shortcomings.

Best High Level API for Tensorflow

Comments: Keras simplifies a lot the designing and manipulation of a Neural Networkìs architecture, making way more accessible the usage of Neural Networks to a wider public. Extremely powerful.

Pros:

This is definitely a user friendly framework to use on top of a Machine Learning library, the obvious choice for me would be to use it alongside Tensorflow.

Cons:

Might looks a bit difficult at first, but if you know the theory behind Neural Networks then you would not have any problem using it for your projects.

A Game-Changer in Deep Learning

Comments: In general, Keras has established itself as a go-to deep learning library for me as a beginner. Its user-friendly API, versatility, extensive documentation, strong community support, performance optimization, and modularity make it a standout choice in the field of deep learning.

Pros:

One of the standout features of Keras is its user-friendly and intuitive API. It offers a high-level abstraction, making it incredibly easy to build and experiment with neural networks. Keras provides an excellent and intuitive experience, allowing me to focus on the core aspects of my models rather than getting pushed down by low-level implementation details. The versatility of Keras is another aspect that sets it apart. It supports both CPU and GPU computations, making it adaptable to various computing environments. Additionally, Keras seamlessly integrates with popular deep learning backends such as TensorFlow and Theano, providing access to an extensive collection of pre-trained models and advanced functionalities.

Cons:

The only issue is lack of flexibility: Keras prioritizes ease of use and abstraction, which can sometimes come at the cost of flexibility. For researchers or practitioners who require fine-grained control over every aspect of their models, Keras may feel restrictive. Certain advanced customization options and low-level operations may not be as easily accessible within the high-level API.

Deep learning in Minutes

Comments: Keras is the engine behind the rapid and fast ML models. It's easy, fast and reliable. I'm currently working on a smart navigation model for the blind and Keras equipped me with every tool I need.

Pros:

Artificial intelligence, Machine learning and deep learning has been the ranking words on Google lately. The need for build smart models for smart devices and smart machine for the future is a necessity. With Keras I build and test deep learning neural networks with few lines of code as compared to the traditional programming. With Keras, i can construct straightforward or complex neural networks inside a couple of moments. Keras is more easy to use.

Cons:

Although it's the best ML framework for begineers, there's not much documentation out there to help newbies.

The more accessible brother of TensorFlow

Pros:

It's very, very easy to build most traditional DL algorithms and train them, even with some modifications.

Cons:

Developing new algorithms might be somewhat more cumbersome than with some of the alternatives, as Keras stays at a pretty high level of abstraction.

Nice framework fo NNs

Pros:

A lot of built-in layer types, easy way to connect them. Our team is using it with Theano and TensorFlow

Cons:

Sometimes you need to do something more complex and Keras is not able to handle it. It is the second you need to switch to Lasagne.

Best available wrapper library for tensor flow and theano backend

Comments: best wrapper library for deep learning or to just bypass the tensorflow

Pros:

This library has made the deep learning's algorithm implementation very easy and fast. It comes with lot of inbuilt functionalities like one hot encoder and lot other data processing stuff. Also, writing the neural network implementation with this library is just few lines of code and very much understandable.

Cons:

There are not much negative of this library but it restrict you to a level of abstraction. If you need to write each bit of your algorithm and customise that then it's better to use tensorflow directly. Other than that it's amazing

Best wrapper library for tensor flow

Comments: Best wrapper library for Theano and Tensorflow

Pros:

I think keras is the best wrapper library for tensor flow. Writing the neural network and other deep learning algorithm in tensorflow is a bit difficult. But with the use of writing all those is very easy. Like you can add convolution layer in just one line. You don't have to worry about the dimension of weight matrix of bias vector, Keras take care of that most of the time.

Cons:

I think it doesn't have any drawbacks. But one think is that if you want to write your own implementation then you have to go back to tensor flow.

Keras is a best library to build our own neural network model

Comments: I have used it to build the convolutional neural network model for my research project.

Pros:

We can build our own neural network architecture using keras without complex codings. The library make it easy to do.

Cons:

Since it doesn't have some useful functionalities and continuously updated. And some times have version problems when we use tensorflow.

Build deep learning prototypes fast

Pros:

Keras allows you to build deep learning models easy and fast using TensorFlow backend, good for beginners in machine learning; Keras also provides several pre-trained models and can implement transfer learning easily.

Cons:

If you need to build a more customized deep learning model, should code in TensorFlow directly.

A great python library for deep learning - used extensively by our innovation team.

Comments: Keras is one of the only real solutions to deep learning and looks great doing it. This is an extensible and very effective solution to building complex machine learning models.

Pros:

Keras is the best library for deep learning machine learning models. It is modular, minimalist and extensible. Python really is the future for machine learning models. It is fast and very advanced in its capability.

Cons:

Learning curve is intense, this is to be expected with emerging technologies so that is the least of our concerns.

Keras an advanced deep learning framework

Comments: Overa experience using keras is best and I have developed multiple CNN based application.

Pros:

What I liked the most about Keras deep learning framework is it is very easy to implement compare to TensorFlow framework, and It has rich inbuilt functions which make the model development more easy and robust

Cons:

What I liked least about Keras is error analysis or error description given by framework, it becomes tough to interpret sometimes.

Keras is a wonderful building tool for neural networks

Comments: I built an industry-based research project using Keras and my friends used other libraries and pure TensorFlow. Compared with them, I completed my project quickly and effectively.

Pros:

It is most compatible with TensorFlow since it can easily use GPU. Also, It has rich tools for text cleaning and we can create any type of neural network architecture easily.

Cons:

It isn't suitable for all systems. It doesn't have pre-defined models like other libraries or tools like Matlab. We can’t modify anything of its backend.