Expert Secrets For Using Rman And Data Pump

Learn how to containerize and deploy your ML projects with Docker

This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve. In addition, this book includes numerous case studies, ranging from simple time-series forecasting to object recognition and recommender systems using massive databases. Lastly, this book also provides practical implementation examples and assignments for the readers to practice and improve their programming capabilities for the ML applications.

This timely book offers a mixture of theory, experiments, and simulations that provides qualitative and quantitative insights in the field of sensor and actuator networking. The chapters are selected in a way that makes the book comprehensive and self-contained. It covers a wide range of recognized problems in sensor networks, striking a balance between theoretical and practical coverage. The book is appropriate for graduate students and practitioners working as engineers, programmers, and technologists.