Deep Learning for Beginners

The aim of the present handbook is to drive the reader toward a deepen knowledge of Deep Learning (DL) methodologies and their functioning.
Even if DL has ancient ancestry, only in recent years they have been strongly revalued and consequently applied. However, it is noteworthy that in this field, the increasing computational power played a fundamental role: Deep Learning models are techniques of success, but, at the same time, they are also expensive from a computational point of view and not always clearly understandable.
The handbook starts considering the “big family” of Artificial Intelligence (AI), and continues clarifying what are Machine Learning (ML) and Deep Learning (DL). The reader will discover that DL is based on Artificial Neural Networks (ANN), then the fundamentals of ANNs will be presented and discussed, until getting to understand the working and the differences among different architectures.
Finally, at the end of the reading, the reader will have in hands all knowledge required for recognizing different Deep Learning architectures, their functioning, and the fields of their applications.