Theano vs scikit-learn

Overview

scikit-learn

Stacks1.3K

Followers1.1K

Votes45

GitHub Stars63.9K

Forks26.4K

Theano

Stacks32

Followers65

Votes0

GitHub Stars10.0K

Forks2.5K

Theano vs scikit-learn: What are the differences?

Primary Purpose: Theano is primarily used for numerical computation, especially in the field of deep learning, allowing users to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. On the other hand, scikit-learn is a machine learning library that provides various tools for data mining and data analysis, focusing on classical machine learning algorithms such as clustering, classification, and regression.
Complexity: Theano offers more flexibility and control over low-level operations, making it suitable for advanced users who require fine-grained control over their algorithms and neural network architectures. In contrast, scikit-learn provides a higher-level interface that simplifies the implementation of machine learning models and pipelines, making it more accessible to beginners and users looking for quick prototyping.
Deep Learning Support: Theano is specifically designed for deep learning research and development, featuring symbolic differentiation, GPU computation, and efficiency optimizations tailored for neural networks. Scikit-learn, while capable of handling various machine learning tasks, lacks the deep learning-specific features and optimizations found in libraries like Theano.
Programming Language: Theano is implemented in Python and tightly integrated with NumPy arrays, leveraging Python's flexibility and expressiveness for mathematical operations. In comparison, scikit-learn is also Python-based but focuses on providing a consistent API and interface for a wide range of machine learning algorithms, making it easier to switch between different models and experiment with various techniques.

In Summary, Theano and scikit-learn cater to different needs in the machine learning landscape, with Theano specializing in deep learning research and intensive numerical computations, while scikit-learn offers a user-friendly interface for traditional machine learning tasks.

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Advice on scikit-learn, Theano

cfvedova

Oct 10, 2020

Decided

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

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Detailed Comparison

scikit-learn	Theano
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.	Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
Statistics
GitHub Stars 63.9K	GitHub Stars 10.0K
GitHub Forks 26.4K	GitHub Forks 2.5K
Stacks 1.3K	Stacks 32
Followers 1.1K	Followers 65
Votes 45	Votes 0
Pros & Cons
Pros 26 Scientific computing 19 Easy Cons 2 Limited	No community feedback yet
Integrations
No integrations available	NumPy Python

What are some alternatives to scikit-learn, Theano?

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.

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

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

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

Polyaxon

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

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

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.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Related Comparisons

Theano vs scikit-learn: What are the differences?

Primary Purpose: Theano is primarily used for numerical computation, especially in the field of deep learning, allowing users to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. On the other hand, scikit-learn is a machine learning library that provides various tools for data mining and data analysis, focusing on classical machine learning algorithms such as clustering, classification, and regression.
Complexity: Theano offers more flexibility and control over low-level operations, making it suitable for advanced users who require fine-grained control over their algorithms and neural network architectures. In contrast, scikit-learn provides a higher-level interface that simplifies the implementation of machine learning models and pipelines, making it more accessible to beginners and users looking for quick prototyping.
Deep Learning Support: Theano is specifically designed for deep learning research and development, featuring symbolic differentiation, GPU computation, and efficiency optimizations tailored for neural networks. Scikit-learn, while capable of handling various machine learning tasks, lacks the deep learning-specific features and optimizations found in libraries like Theano.
Programming Language: Theano is implemented in Python and tightly integrated with NumPy arrays, leveraging Python's flexibility and expressiveness for mathematical operations. In comparison, scikit-learn is also Python-based but focuses on providing a consistent API and interface for a wide range of machine learning algorithms, making it easier to switch between different models and experiment with various techniques.

Theano vs scikit-learn

Overview