Keras vs Paperspace

Overview

Keras

Stacks1.1K

Followers1.1K

Votes22

Paperspace

Stacks4

Followers20

Votes0

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.

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Advice on Keras, Paperspace

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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Detailed Comparison

Keras	Paperspace
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/	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.
neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU	Intelligent alert; Two-factor authentication; Share drives; Unlimited power; Multiple monitors; Remote access; Simple management.
Statistics
Stacks 1.1K	Stacks 4
Followers 1.1K	Followers 20
Votes 22	Votes 0
Pros & Cons
Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug	No community feedback yet
Integrations
TensorFlow scikit-learn Python	Golang Swift Postman Airtable Azure IoT Hub

What are some alternatives to Keras, Paperspace?

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.

scikit-learn

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

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.

NanoNets

Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

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.

Inferrd

It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

Related Comparisons

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.

Keras vs Paperspace