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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Numba vs Theano

Numba vs Theano

OverviewComparisonAlternatives

Overview

Theano
Theano
Stacks32
Followers65
Votes0
GitHub Stars10.0K
Forks2.5K
Numba
Numba
Stacks20
Followers44
Votes0
GitHub Stars0
Forks0

Theano vs Numba: What are the differences?

Developers describe Theano as "Define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently". Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C impleme. On the other hand, Numba is detailed as "An open source JIT compiler that translates a subset of Python and NumPy code into fast machine code". It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.

Theano and Numba belong to "Machine Learning Tools" category of the tech stack.

Theano is an open source tool with 8.89K GitHub stars and 2.5K GitHub forks. Here's a link to Theano's open source repository on GitHub.

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Detailed Comparison

Theano
Theano
Numba
Numba

Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).

It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.

-
On-the-fly code generation; Native code generation for the CPU (default) and GPU hardware; Integration with the Python scientific software stack
Statistics
GitHub Stars
10.0K
GitHub Stars
0
GitHub Forks
2.5K
GitHub Forks
0
Stacks
32
Stacks
20
Followers
65
Followers
44
Votes
0
Votes
0
Integrations
NumPy
NumPy
Python
Python
C++
C++
TensorFlow
TensorFlow
Python
Python
GraphPipe
GraphPipe
Ludwig
Ludwig

What are some alternatives to Theano, Numba?

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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