Keras

4.7
(13)

Keras is a neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

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Showing 13 Keras reviews
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Keras review by Bhuvan P.
Bhuvan P.
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"Deep Learning Simplified"

What do you like best?

The API they provide are simple to use and are easy to understand with the detailed documentation available on the official Keras website. Generally Deep Learning Models are complex and hard to implement but with Keras API a newbie can also build his/her own model with great ease.

What do you dislike?

Keras is a amazing Deep Learning library that fits with TensorFlow and many more Deep Learning libraries but the disadvantage of Keras is that it is only available for Python whereas TensorFlow and the other libraries have multi-language support.

Recommendations to others considering the product

One of the best Deep Learning library for Python with a huge community support you can easily find solution to your quires, here is one of the forums I follow to do so: https://groups.google.com/forum/#!forum/keras-users

What business problems are you solving with the product? What benefits have you realized?

We, TeckGeeks are a Machine Learning and Deep Learning startup. We are interested in building ML and DL models for many different tasks. We build models that fits our client requests.

With Keras it is easy to achieve so as we can concentrate more on the overall application and not the model, as Keras serves this by providing simple and consistent API.

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Keras review by Muhammad Usman G.
Muhammad Usman G.
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"Extremely Useful DL framework for beginners"

What do you like best?

Keras is the best deep-learning framework for beginner level data scientists and machine-learning/artificial intelligence researchers. I like its high-level features that one can use to program a neural network, for instance, write an image classification task in a few lines. Additionally, the availability of pre-trained networks is a plus which allows practitioners to use DL models even without the need for training or an intelligent initialization for researchers. The feature I like the most is high-level function for training, which automatically keeps track of training and validation loss and also keeps an estimate of training progress. Loss functions are already implemented, so in most cases one do not have to implement these functionalities by themseleves. Training and validation set queues are an efficient way of data handling and that feature is already implemented in Keras.

What do you dislike?

I only have one complaint that once you start working with Keras it is difficult for researchers to move to Tensorflow type libraries where one can implement flexible functionalities specialized to one's research.

What business problems are you solving with the product? What benefits have you realized?

I use Keras for Deep Learning/ Artificial Intelligence research, where I develop DL based methods for computer vision and computational imaging.

What Artificial Neural Network solution do you use?

Thanks for letting us know!
Keras review by Mohit S.
Mohit S.
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"High Level Deep Learning API"

What do you like best?

The API they provide is efficient where one can make ones own deep learning models with only some basic knowledge. Also they provide support for other machine learning/deep learning libraries like TensorFlow which really makes work easy. One more thing I would like to mention is that they provide module wise API, which means one can play around with each module while building a deep net like the layers, activation functions, cost functions, e.t.c.

What do you dislike?

This library is pretty good and only option for me to carry out my deep learning tasks. So I personally have no dislikes for it.

Recommendations to others considering the product

The documentation is a great source, they provide detailed information of the API along with some good examples. Try to go for those examples first, they provide enough understanding for one to start with Keras.

What business problems are you solving with the product? What benefits have you realized?

Building Deep Learning Models, actually Keras has made the work easy as one can manipulate it every level. So we can really customise and produce a model that perfectly meets the needs of clients.

Keras review by G2 Crowd User in Computer Software
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"Keras: Tensorflow made simpler"

What do you like best?

Keras makes working with Tensorflow and doing deep-learning very easy: it provides an user friendly API that hides most of the complexity of using TF. Models can be quickly defined by piping Keras layers together and tweaking the parameters through the layer constructor. The learning process can be easily customized by tweaking the various rates. Since training a deep-learning model takes a lot of time/data and things can easily go wrong, you will appreciate the availability of the training callbacks: the ReduceLROnPlateau, ModelCheckpoint or TensorBoard, to name a few. Definitely a library the must be used for quickly putting together prototypes before diving too much into the details.

What do you dislike?

Keras doesn't make it easier to debug your models: but this is not a problem strictly tied to the library and it's common in the deep learning world.

Recommendations to others considering the product

If you need to quickly put together a prototype for a new idea, Keras is the go-to library to get started. You might need to do without it to have a greater control over your model in the next phases, but this is definitely the way to start.

Documentation is widely available and there is plenty of tutorials to get yourself up to speed with the API.

What business problems are you solving with the product? What benefits have you realized?

Training a deep-learning model for performing anomaly detection in time series and recommendation systems.

Keras review by G2 Crowd User
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"I love this ML toolkit!"

What do you like best?

Keras makes making machine learning models super easy. It has an easy to learn syntax that runs on top of Tensorflow which is the backend I use, which compiles down into native Tensorflow that allows the user to create fast performing machine learning models without worrying about the exact Tensorflow syntax, which can get quite complicated at times.

What do you dislike?

Sometimes, if you want to do something custom, you really can't unless you go down to the base Tensorflow layer and add raw Tensorflow code, which isn't too great. There should be greater extensibility within Keras itself.

Recommendations to others considering the product

Learn basic machine learning first, or even advanced deep learning, as otherwise the product will not teach you.

What business problems are you solving with the product? What benefits have you realized?

We can build our machine learning models and pipelines in order to solve client problems, such as with agricultural yield prediction for the coming seasons.

Keras review by G2 Crowd User in Consumer Services
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"Excellent Platform for Deep Learning"

What do you like best?

The Keras API makes experimenting with deep learning models a breeze. It's easy to stack layers and to experiment with different deep learning architectures for a wide variety of problems including natural language processing and activity recognition.

What do you dislike?

Keras is still in development. Although it works great the majority of the time, don't be surprised if you find a bug or that a specific architecture/deep learning layer you want to use is not implemented yet.

Recommendations to others considering the product

There are plenty of tutorials online on how to get started with Keras. However, it's always good to understand the math behind any neural network architecture before implementing it to make it easier to debug and understand why it does what it does.

What business problems are you solving with the product? What benefits have you realized?

I have used Keras to build natural language processing models. The application of these models has helped us to realize better engagement with our users and better performance from our employees.

Keras review by G2 Crowd User in Internet
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"Very Useful Tensorflow Wrapper"

What do you like best?

Keras drastically simplifies building neural networks in Tensorflow without losing too much customizability/functionality. I prefer Keras over TF always unless I know for sure that the best way to use some aspect is within TF itself.

What do you dislike?

For beginners, bypassing concepts like matrix multiplication/dimensions can make some errors hard to rectify. So while it's a great way to get your feet wet building NNs, some coding with Tensorflow can be useful to keep your shapes in line.

Recommendations to others considering the product

If you are not already familiar with matrix multiplication and dimensions/shapes, I recommend familiarizing yourself with these concepts in TF before jumping into Keras.

What business problems are you solving with the product? What benefits have you realized?

I primarily use Keras to build neural nets for financial prediction and natural language processing.

Keras review by Subit C.
Subit C.
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"Usability: Maximum, Configurability: Minimum"

What do you like best?

Keras is all about ease of use. It should take a true beginner (someone who theoretically understands neural network but has never coded one) less than an hour to code their first network. And it works ! I also like that it works with both Theano and Tensorflow (although that might change soon).

What do you dislike?

There is minimum configurability and things that are essential and easily accomplished in other frameworks takes a while. For example, building a layer that is not in one of the predefined templates or checking the weights on each layers.

Recommendations to others considering the product

Keras is super easy to use and a good way to learn neural networks. However, once you start building complicated models/networks Keras is rarely enough

What business problems are you solving with the product? What benefits have you realized?

Keras tries to simplify the building of deep neural networks. Its audience is people who are not hardcore software engineers/data scientists who breathe Tensorflow/Theano every day. This it does perfectly.

Keras review by Zachi A.
Zachi A.
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"Keras for convolutional and recurrent neural network "

What do you like best?

Fast and flexible way of testing and building different networks, easy loading and saving of model and training with a similar interface to sklearn.

What do you dislike?

When using with tensor board (with TF background) model looks clumsy, model is shown with additional blocks

Recommendations to others considering the product

Just use it ! after you found your perfect model you can always rebuild and retrain with a different framework that is easier to implement online

What business problems are you solving with the product? What benefits have you realized?

Building CNNs and RNNs for medical predications.

I am using different Deep Learning frameworks for a while and this is by far the bast for RESEARCH. you can test and change you networks in a modular way allowing you to make much faster research

Keras review by Prabhash T.
Prabhash T.
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"Deep Learning On the Go"

What do you like best?

The best thing about Keras is it can work with the well known deep learning and machine learning libraries like TensorFlow and Theano. The API is very easy to use and one can create a whole CNN with less than 20 lines of code.

What do you dislike?

It is the best deep learning library I have ever found out for python, no dislikes.

Recommendations to others considering the product

This a video from famous Data Science Enthusiast, Siraj Raval : https://www.youtube.com/watch?v=j_pJmXJwMLA , watch it he explains every bit of Keras.

What business problems are you solving with the product? What benefits have you realized?

Developing CNN, RNN and other deep learning models to carry out various tasks in our organizations.

Keras review by G2 Crowd User
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"Simple platform to develop deep learning model"

What do you like best?

Very simple, clean and easy framework to implement deep learning models compared to tensorflow, caffe and PyTorch

What do you dislike?

Even though it provides a simple interface and high-level implementation of basic deep learning blocks it becomes difficult for custom functions

What business problems are you solving with the product? What benefits have you realized?

Computer Vision, Deep Learning, Text to Speech Synthesis, Object Detection, Image Classification

Keras review by G2 Crowd User
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"Very recomendable for deep learning in medical imaging"

What do you like best?

Different backends, relies on python. Works in 3D images. Is easy to install.

What do you dislike?

Since it is python based, it's not compatible with 3d image visualisation. When using tensor flow, the only way to follow the training is using tensor board, which is slow.

What business problems are you solving with the product? What benefits have you realized?

Medical image a analysis in 3d

Keras review by G2 Crowd User in Telecommunications
G2 Crowd User in Telecommunications
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"Easy and flexible"

What do you like best?

Easy setup, flexibility, different backends, python-based

What do you dislike?

There's no interface such as nvidia digits. There is tensorboard but it's slow and not as intuitive as digits

What business problems are you solving with the product? What benefits have you realized?

Medical Image Analysis. Working with 3D images easily.

Kate from G2 Crowd

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