In this chapter, we have come quite a long way covering the TensorFlow landscape and its corresponding methods. We got acquainted with how to set up basic regressors, classifiers, and single-hidden layer neural networks. Even though the programming TensorFlow operations are relatively straightforward, for off-the-shelf machine learning tasks, TensorFlow might be a little bit too tedious. This is exactly where SkFlow comes in, a higher-level library with an interface quite similar to Scikit-learn. For incremental or even out-of-core solutions, SkFlow provides a partial fit method, which can easily be set up. Other large scale solutions are either restricted to GPU applications or are at a premature stage. So for now, we have to settle for incremental learning strategies when it comes to scalable solutions.
We also provided an introduction to Convolutional Neural Networks and saw how they can be set up in Keras.
