Keras vs MXNet: What are the differences?
What is Keras? Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/.
What is MXNet? A flexible and efficient library for deep learning. A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
Keras and MXNet belong to "Machine Learning Tools" category of the tech stack.
Some of the features offered by Keras are:
- neural networks API
- Allows for easy and fast prototyping
- Convolutional networks support
On the other hand, MXNet provides the following key features:
- Flexible distributed/Mobile deep learning
Keras and MXNet are both open source tools. It seems that Keras with 43.2K GitHub stars and 16.5K forks on GitHub has more adoption than MXNet with 17.5K GitHub stars and 6.21K GitHub forks.
What is Keras?
What is MXNet?
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Why do developers choose MXNet?
What are the cons of using MXNet?
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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:
(Direct GitHub repo: https://github.com/uber/horovod)