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.
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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