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Is the TensorFlow designed only for implementing neural networks? Can it be used as a general machine learning library -- for implementing all sorts of supervised as well as unsupervised techniques (naive baysian, decision trees, k-means, SVM to name a few) ? Whatever TensorFlow literature I am coming across is generally talking about neural networks. Probably graph based architecture of TensorFlow makes it suitable candidate for neural nets. But can it be also used as a general Machine Learning framework?
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2) No supports for windows: There is a wide variety of users who are comfortable in a window environment rather than Linux, and TensorFlow doesn't satisfy these users. But we need not worry about that if we are a window user we can also install it through conda or python package library (pip).
They were only expecting several popular types of deep learning algorithms from the code base as heard from other people and social media. Yet, TensorFlow is not just for deep learning. It provides a great variety of building blocks for general numerical computation and machine learning.
TensorFlow is a framework created by Google for creating Deep Learning models. Deep Learning is a category of machine learning models (=algorithms) that use multi-layer neural networks. Machine Learning has enabled us to build complex applications with great accuracy.
By the end of this course, you will gain significant hands-on experience using unsupervised learning algorithms with TensorFlow and will be able to make your own model to solve relevant real-world learning problems.
Tensorflow does include additional machine learning algorithms such as:
K-means clustering
Random Forests
Support Vector Machines
Gaussian Mixture Model clustering
Linear/logistic regression
The above list is taken from here, so you can read this link for more details.
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