Deep learning with theanets
Scikit-learn's neural network application is especially interesting for parameter tuning purposes. Unfortunately, its capabilities for unsupervised neural network applications are limited. For the next subject, where we dive into more sophisticated deep learning methods, we need another library. In this chapter, we will focus on theanets. We love theanets because of its ease of use and stability; it's a very smooth and well-maintained package developed by Lief Johnson at the University of Texas. Setting up a neural network architecture works quite similarly to sklearn; namely, we instantiate a learning objective (classification or regression), specify the layers, and train it. For more information, you can visit http://theanets.readthedocs.org/en/stable/ ...
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