scikit-learn vs TensorFlow

scikit-learn

691
686
+ 1
29
TensorFlow

2K
2.1K
+ 1
72
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scikit-learn vs TensorFlow: What are the differences?

What is scikit-learn? Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

What is TensorFlow? Open Source Software Library for Machine Intelligence. 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 and TensorFlow belong to "Machine Learning Tools" category of the tech stack.

"Scientific computing" is the top reason why over 14 developers like scikit-learn, while over 15 developers mention "High Performance" as the leading cause for choosing TensorFlow.

scikit-learn is an open source tool with 35.7K GitHub stars and 17.4K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.

According to the StackShare community, TensorFlow has a broader approval, being mentioned in 195 company stacks & 126 developers stacks; compared to scikit-learn, which is listed in 70 company stacks and 39 developer stacks.

Advice on scikit-learn and TensorFlow
Adithya Shetty
Student at PES UNIVERSITY · | 5 upvotes · 46.5K views
Needs advice
on
TensorFlow
PyTorch
and
Keras

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!!

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Pros of scikit-learn
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Cons of scikit-learn
Cons of TensorFlow

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What is scikit-learn?

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

What is 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.
What companies use scikit-learn?
What companies use TensorFlow?

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What tools integrate with scikit-learn?
What tools integrate with TensorFlow?

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What are some alternatives to scikit-learn and TensorFlow?
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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.
XGBoost
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
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.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
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