The Criminal Justice Technology Forecasting Group (CJTFG) deliberated on the effects that major technology and social trends could have on criminal justice in the next two to five years and identified potential responses. This report captures the results of the group’s meetings and initiatives, presents the emerging trends and highlights of the group’s discussion, and presents the results of analyses to assess connections between the trends.
Given the challenges posed to the U.S. corrections sector, such as tightened budgets and increasingly complex populations under its charge, it is valuable to identify opportunities where changes in tools, practices, or approaches could improve performance. In this report, RAND researchers, with the help of a practitioner Corrections Advisory Panel, seek to map out an innovation agenda for the sector.
This book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising. The design of the DL models based on artificial neural networks is influenced by the structure and operation of the brain. This book presents a comprehensive resource for those who seek a solid grasp of the techniques in DL. Key features: • Provides knowledge on theory and design of state-of-the-art deep learning models for real-world applications. • Explains the concepts and terminology in problem-solving with deep learning. • Explores the theoretical basis for major algorithms and approaches in deep learning. • Discusses the enhancement techniques of deep learning models. • Identifies the performance evaluation techniques for deep learning models. Accordingly, the book covers the entire process flow of deep learning by providing awareness of each of the widely used models. This book can be used as a beginners’ guide where the user can understand the associated concepts and techniques. This book will be a useful resource for undergraduate and postgraduate students, engineers, and researchers, who are starting to learn the subject of deep learning.
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