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  5. .NET for Apache Spark vs NLTK

.NET for Apache Spark vs NLTK

OverviewComparisonAlternatives

Overview

NLTK
NLTK
Stacks136
Followers179
Votes0
.NET for Apache Spark
.NET for Apache Spark
Stacks31
Followers46
Votes0
GitHub Stars2.1K
Forks329

.NET for Apache Spark vs NLTK: What are the differences?

.NET for Apache Spark: Makes Apache Spark™ Easily Accessible to .NET Developers. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data; NLTK: It is a leading platform for building Python programs to work with human language data. It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

.NET for Apache Spark and NLTK belong to "Machine Learning Tools" category of the tech stack.

.NET for Apache Spark is an open source tool with 1.16K GitHub stars and 119 GitHub forks. Here's a link to .NET for Apache Spark's open source repository on GitHub.

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Detailed Comparison

NLTK
NLTK
.NET for Apache Spark
.NET for Apache Spark

It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data.

Statistics
GitHub Stars
-
GitHub Stars
2.1K
GitHub Forks
-
GitHub Forks
329
Stacks
136
Stacks
31
Followers
179
Followers
46
Votes
0
Votes
0
Integrations
No integrations available
Apache Spark
Apache Spark
.NET
.NET
F#
F#
C#
C#
Ubuntu
Ubuntu

What are some alternatives to NLTK, .NET for Apache Spark?

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.

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.

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.

Keras

Keras

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

Kubeflow

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

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

Polyaxon

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

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.

MLflow

MLflow

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

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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