Alternative development tools

Over the last couple of years, a number of new machine learning frameworks have emerged that offer advantages in terms of workflow. Usually these frameworks are highly focused on a specific use case or objective. This makes them very useful, perhaps even must-have tools, but it also means that you may need to use multiple workflow improvement libraries.

With an ever-growing set of new Python ML projects being lit up to address specific workflow challenges, it's worth discussing two libraries that add to our existing workflow and which accelerate or improve the work we've done in the preceding chapters. In this chapter, we'll be introducing Lasagne and TensorFlow, discussing the code and capabilities of each library and identifying why each framework is worth considering as a part of your toolset.

Introduction to Lasagne

Let's face it; sometimes creating models in Python takes longer than we'd like. However, they can be efficient for models that are more complex...