Keras vs Paperspace: What are the differences?
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/; Paperspace: The way to access and manage limitless computing power in the cloud. It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
Keras and Paperspace 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, Paperspace provides the following key features:
- Intelligent alert
- Two-factor authentication
- Share drives
Keras is an open source tool with 44.1K GitHub stars and 16.8K GitHub forks. Here's a link to Keras's open source repository on GitHub.
What is Keras?
What is Paperspace?
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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)