This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs).

Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how ...

This book is very beneficial for early researchers/faculty who want to work in deep learning and machine learning for the classification domain.

... neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101 (2016) 16. Long, S ... large-scale heterogeneous text networks. In: KDD, pp. 1165–1174. ACM (2015) 22. Wang, G., et al.: Joint ...

... deeper architectures are deserved to attempt to use as the backbone of SA-Attn-CNN, when we have larger scale ... text classification based on LDA and BILSTM- CNN neural network. In: WISA (2019) 3. Tsantekidis, A., et al.: Forecasting ...

... convolutional neural networks (Krizhevsky et al. 2012; Simonyan and Zisserman 2014; Szegedy et al. 2015). Deep networks minimize a non-convex loss function, thus obtaining impressive levels of performance when very large training data ...

This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines.

Deep Learning with Keras This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks ...

This book gives a concise view on how to use kernel approaches for inductive inference in large scale text classification; it presents a series of new techniques to enhance, scale and distribute text classification tasks.

This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis.