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What all should I learn in sequence to be expert in Tensorflow and Google Keras provided I know Python and Numpy, Pandas, Pyplot basics? Please provide the resources too as most online tutorials are chargeable

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  • $\begingroup$ Searching Google and the Docs might help... $\endgroup$
    – Aditya
    Apr 5, 2018 at 18:13

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Take a look at professor Andrew Ng's course about deep learning. Its homework is written in Keras and TensorFlow. It is a very good point to begin.


I guess it is not fair to mention non-free lectures because the owners of other non-free lectures may get disappointed but take a look at complete guide to TensorFlow for deep learning with python, which is very useful.
I have also found these lectures from Stanford university very useful. Furthermore, you can take a look at the official paper of google research team about TensorFlow where you can read TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org..

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