**Preface to "Innovative Topologies and Algorithms for Neural Networks"**

Interest in the study of deep neural networks in the field of neural computation has increased, both as it regards to new training procedures and topologies, as well as significant applications. In particular, greater attention is being paid to challenging applications that are not adequately addressed by classical machine learning methods. Consequently, the use of deep structures has significantly improved state-of-the-art applications in many fields, such as object and gesture recognition, speech and language processing, and the Internet of Things (IoT). This book is comprised of discussions and analyses of relevant applications in the fields of speech and text analysis, object and gesture recognition, medical applications, IoT implementations, and sentiment analysis. Successful solutions to complex problems, such as those examined in the contributions noted above, are closely linked to identifying suitable network architectures. In this book, long short-term memory (LSTM) and convolutional neural network (CNN)-derived architectures are the most commonly used neural structures. Furthermore, in many of the contributions, a deep interplay exists between the adopted neural structures and the investigated application, leading to the proposal of tailored architectures. The authors give significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.
