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  1. Stackups
  2. DevOps
  3. Build Automation
  4. Python Build Tools
  5. ENorm vs XGBoost

ENorm vs XGBoost

OverviewComparisonAlternatives

Overview

XGBoost
XGBoost
Stacks192
Followers86
Votes0
GitHub Stars27.6K
Forks8.8K
ENorm
ENorm
Stacks1
Followers9
Votes0
GitHub Stars115
Forks13

ENorm vs XGBoost: What are the differences?

Developers describe ENorm as "Equi-normalization of Neural Networks (by Facebook)". A fast and iterative method for minimizing the L2 norm of the weights of a given neural network that provably converges to a unique solution. On the other hand, XGBoost is detailed as "Scalable and Flexible Gradient Boosting". Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow.

ENorm and XGBoost belong to "Machine Learning Tools" category of the tech stack.

ENorm is an open source tool with 108 GitHub stars and 9 GitHub forks. Here's a link to ENorm's open source repository on GitHub.

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Detailed Comparison

XGBoost
XGBoost
ENorm
ENorm

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

A fast and iterative method for minimizing the L2 norm of the weights of a given neural network that provably converges to a unique solution.

Flexible; Portable; Multiple Languages; Battle-tested
Asymmetric scaling; Python 3.6 and latest support;
Statistics
GitHub Stars
27.6K
GitHub Stars
115
GitHub Forks
8.8K
GitHub Forks
13
Stacks
192
Stacks
1
Followers
86
Followers
9
Votes
0
Votes
0
Integrations
Python
Python
C++
C++
Java
Java
Scala
Scala
Julia
Julia
No integrations available

What are some alternatives to XGBoost, ENorm?

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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