Is TensorFlow a complete Machine Learning Library? I am new to TensorFlow and I need to understand the capabilities and shortcomings of TensorFlow before I can use it. I know that it is a deep learning framework, but apart from that which other machine learning algorithms can we use with tensor flow. For example can we use SVMs or random forests using TensorFlow? (I know this sounds crazy)

In short, I want to know which Machine Learning Algorithms are supported by TensorFlow. Is it just deep learning or something more?
This is a big oversimplification, but there are essentially two types of machine learning libraries available today: 


Deep learning (CNN,RNN, fully connected nets, linear models)
Everything else (SVM, GBMs, Random Forests, Naive Bayes, K-NN, etc)


The reason for this is that deep learning is much more computationally intensive than other more traditional training methods, and therefore requires intense specialization of the library (e.g., using a GPU and distributed capabilities). If you're using Python and are looking for a package with the greatest breadth of algorithms, try scikit-learn. In reality, if you want to use deep learning and more traditional methods you'll need to use more than one library. There is no "complete" package.