Conclusion

Until now, we have discussed the majority of the techniques, concepts, and methods that fall into the realm of advanced practices when building deep learning models for Python. We have explored the intricacies of the neural network in a mode. We also explored its internal realms by experimenting on how we can change its structure, add in different layers, functions, and arguments and other such elements to build a model that effectively works to perform the task at hand efficiently and optimally. To wrap things up, we introduced a final bundle of techniques that require practice to master and ingenuity to implement correctly, but when used correctly, they will prove to be the building blocks of a deep learning model that will foster the aspirations of the engineer in its truest form.

As a final note, we could only cover so many topics in the span of one book, in the upcoming series we will delve even deeper into the complex and intricate understandings of deep learning systems and rebuild our current understanding into something even grander.