Keras vs scikit-learn: What are the differences?
Developers describe Keras as "Deep Learning library for Theano and TensorFlow". Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/. On the other hand, scikit-learn is detailed as "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.
Keras and scikit-learn can be primarily classified as "Machine Learning" tools.
"Easy and fast NN prototyping" is the primary reason why developers consider Keras over the competitors, whereas "Scientific computing" was stated as the key factor in picking scikit-learn.
Keras and scikit-learn are both open source tools. Keras with 42.5K GitHub stars and 16.2K forks on GitHub appears to be more popular than scikit-learn with 36K GitHub stars and 17.6K GitHub forks.
Repro, Home61, and MonkeyLearn are some of the popular companies that use scikit-learn, whereas Keras is used by StyleShare Inc., Home61, and Suggestic. scikit-learn has a broader approval, being mentioned in 71 company stacks & 40 developers stacks; compared to Keras, which is listed in 52 company stacks and 50 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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