Kaggle vs Paperspace

Overview

Paperspace

Stacks4

Followers20

Votes0

Kaggle

Stacks10

Followers7

Votes0

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Detailed Comparison

Paperspace	Kaggle
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.	Machine learners share, stress test, and stay up-to-date on all the latest ML techniques and technologies. Discover a huge repository of community-published models, data & code for your next project.
Intelligent alert; Two-factor authentication; Share drives; Unlimited power; Multiple monitors; Remote access; Simple management.	Find all the resources and knowledge needed for your next real-world ML project; High-quality public datasets. Everything from avocado prices to video game sales; Public notebooks and access to a powerful notebook environment with no cost GPUs & TPUs; Pre-trained, ready-to-deploy ML models; Build your ML skills in a supportive and helpful community
Statistics
Stacks 4	Stacks 10
Followers 20	Followers 7
Votes 0	Votes 0
Integrations
Golang Swift Postman Airtable Azure IoT Hub	Jupyter Python

What are some alternatives to Paperspace, Kaggle?

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.

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Keras

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

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

NumPy

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.