TensorFlow vs scikit-learn

Overview

scikit-learn

Stacks1.3K

Followers1.1K

Votes45

GitHub Stars63.9K

Forks26.4K

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

TensorFlow vs scikit-learn: What are the differences?

Introduction:

When it comes to machine learning and deep learning libraries, TensorFlow and scikit-learn are two popular choices that serve different purposes. Understanding the key differences between these two libraries can help practitioners choose the right tool for their specific tasks.

Data Types: TensorFlow is primarily focused on deep learning tasks and works well with tensor data structures. On the other hand, scikit-learn is more versatile and capable of handling various data types including numerical, categorical, and textual data. This makes scikit-learn a preferred choice for machine learning tasks beyond deep learning.
Nature of Algorithms: TensorFlow is tailored towards implementing neural networks and deep learning models, making it a go-to tool for complex neural network architectures. In contrast, scikit-learn is designed for traditional machine learning algorithms such as regression, classification, clustering, and dimensionality reduction. This difference in focus dictates the type of tasks each library is best suited for.
Ease of Use: Scikit-learn is renowned for its user-friendly API and ease of implementation, making it a popular choice for beginners and rapid prototyping. On the other hand, TensorFlow's complexity stems from its deep learning capabilities, requiring a more advanced understanding of neural networks and computational graphs.
Community Support: Scikit-learn boasts a larger and more established community compared to TensorFlow, which translates to extensive documentation, tutorials, and support forums. This community-driven aspect of scikit-learn facilitates learning and problem-solving for users at all levels.
Deployment Flexibility: TensorFlow provides more options for deploying models in production environments, especially when it comes to deploying deep learning models in production-ready systems. Its integration with tools like TensorFlow Serving and TensorFlow Lite offers enhanced deployment capabilities compared to scikit-learn.
Performance and Scalability: TensorFlow is optimized for scalability and performance, particularly in training large deep neural networks on distributed computing systems. This scalability advantage makes TensorFlow suitable for handling big data and running computationally intensive computations efficiently compared to scikit-learn.

In Summary, understanding the key differences between TensorFlow and scikit-learn can guide practitioners in selecting the most suitable library for their machine learning and deep learning tasks.

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CLI (Node.js)

Manual

Advice on scikit-learn, TensorFlow

Developer at DCSIL

Oct 11, 2020

Decided

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

99.3k views99.3k

Comments

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.64k views4.64k

Comments

Detailed Comparison

scikit-learn	TensorFlow
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.	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.
Statistics
GitHub Stars 63.9K	GitHub Stars 192.3K
GitHub Forks 26.4K	GitHub Forks 74.9K
Stacks 1.3K	Stacks 3.9K
Followers 1.1K	Followers 3.5K
Votes 45	Votes 106
Pros & Cons
Pros 26 Scientific computing 19 Easy Cons 2 Limited	Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful
Integrations
No integrations available	JavaScript

What are some alternatives to scikit-learn, TensorFlow?

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.

Gluon

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

Related Comparisons

TensorFlow vs scikit-learn: What are the differences?

Introduction:

Data Types: TensorFlow is primarily focused on deep learning tasks and works well with tensor data structures. On the other hand, scikit-learn is more versatile and capable of handling various data types including numerical, categorical, and textual data. This makes scikit-learn a preferred choice for machine learning tasks beyond deep learning.
Nature of Algorithms: TensorFlow is tailored towards implementing neural networks and deep learning models, making it a go-to tool for complex neural network architectures. In contrast, scikit-learn is designed for traditional machine learning algorithms such as regression, classification, clustering, and dimensionality reduction. This difference in focus dictates the type of tasks each library is best suited for.
Ease of Use: Scikit-learn is renowned for its user-friendly API and ease of implementation, making it a popular choice for beginners and rapid prototyping. On the other hand, TensorFlow's complexity stems from its deep learning capabilities, requiring a more advanced understanding of neural networks and computational graphs.
Community Support: Scikit-learn boasts a larger and more established community compared to TensorFlow, which translates to extensive documentation, tutorials, and support forums. This community-driven aspect of scikit-learn facilitates learning and problem-solving for users at all levels.
Deployment Flexibility: TensorFlow provides more options for deploying models in production environments, especially when it comes to deploying deep learning models in production-ready systems. Its integration with tools like TensorFlow Serving and TensorFlow Lite offers enhanced deployment capabilities compared to scikit-learn.
Performance and Scalability: TensorFlow is optimized for scalability and performance, particularly in training large deep neural networks on distributed computing systems. This scalability advantage makes TensorFlow suitable for handling big data and running computationally intensive computations efficiently compared to scikit-learn.

In Summary, understanding the key differences between TensorFlow and scikit-learn can guide practitioners in selecting the most suitable library for their machine learning and deep learning tasks.

TensorFlow vs scikit-learn

Overview