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API StatusChangelog
Keras
ByKerasKeras

Keras

#8in Development & Training Tools
Discussions2
Followers1.13k
OverviewDiscussions2Adoption

What is Keras?

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Keras is a tool in the Development & Training Tools category of a tech stack.

Key Features

neural networks APIAllows for easy and fast prototypingConvolutional networks supportRecurent networks supportRuns on GPU

Keras Pros & Cons

Pros of Keras

  • ✓Quality Documentation
  • ✓Easy and fast NN prototyping
  • ✓Supports Tensorflow and Theano backends

Cons of Keras

  • ✗Hard to debug

Keras Alternatives & Comparisons

What are some alternatives to Keras?

TensorFlow

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

CUDA

CUDA

A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

Torch

Torch

It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Try It

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Keras Integrations

Deepo, TensorFlow, scikit-learn, Python, Polyaxon and 7 more are some of the popular tools that integrate with Keras. Here's a list of all 12 tools that integrate with Keras.

Deepo
Deepo
TensorFlow
TensorFlow
scikit-learn
scikit-learn
Python
Python
Polyaxon
Polyaxon
Lobe
Lobe
Data Miner
Data Miner
Caffe
Caffe
Cortex.dev
Cortex.dev
Neptune
Neptune
Deepkit
Deepkit
Jovian
Jovian

Keras Discussions

Discover why developers choose Keras. Read real-world technical decisions and stack choices from the StackShare community.

adarsh pandey
adarsh pandey

Apr 26, 2020

Needs adviceonDjangoDjangoKerasKerasTensorFlowTensorFlow

I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

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Conor Myhrvold
Conor Myhrvold

Tech Brand Mgr, Office of CTO at Uber Technologies

Dec 4, 2018

Needs adviceonTensorFlowTensorFlowKerasKerasPyTorchPyTorch

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

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