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  5. NLTK vs ml5.js

NLTK vs ml5.js

OverviewComparisonAlternatives

Overview

NLTK
NLTK
Stacks136
Followers179
Votes0
ml5.js
ml5.js
Stacks5
Followers53
Votes0
GitHub Stars6.6K
Forks908

ml5.js vs NLTK: What are the differences?

ml5.js: Friendly machine learning for the web. ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies; 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.

ml5.js and NLTK can be primarily classified as "Machine Learning" tools.

ml5.js is an open source tool with 2.82K GitHub stars and 222 GitHub forks. Here's a link to ml5.js's open source repository on GitHub.

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Detailed Comparison

NLTK
NLTK
ml5.js
ml5.js

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

ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.

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Pre-trained models for detecting human poses, generating text, styling an image with another, composing music, pitch detection, and common English language word relationships; API for training new models based on pre-trained ones as well as training from custom user data from scratch
Statistics
GitHub Stars
-
GitHub Stars
6.6K
GitHub Forks
-
GitHub Forks
908
Stacks
136
Stacks
5
Followers
179
Followers
53
Votes
0
Votes
0

What are some alternatives to NLTK, ml5.js?

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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