Keras vs TensorFlow vs scikit-learn: What are the differences?
Tensorflow is the most famous library in production for deep learning models. Offers automatic differentiation to perform backpropagation smoothly, allowing you to literally build any machine learning model literally. Keras is a high-level API built on Tensorflow. It is user-friendly and helps quickly build and test a neural network with minimal lines of code. Like building simple or complex neural networks within a few minutes. Modular since everything in Keras can be represented as modules. Scikit Learn is a general machine learning library built on top of NumPy. It features a lot of utilities for general pre and post-processing of data. It is a library in Python used to construct traditional models.
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!!