Ubuntu 11 04 Installation Guide

The official "Ubuntu 11.04 Installation Guide" contains installation instructions for the Ubuntu 11.04 system (codename "Natty Narwhal").

The official "Ubuntu 11.04 Server Guide" contains information on how to install and configure various server applications on your Ubuntu system to fit your needs.

The official "Ubuntu 11.04 Classical Desktop Guide" contains information on how to using Ubuntu in a desktop environment (Classical desktop).

Arguably one of the most highly regarded and widely used enterprise level operating systems available today is the Ubuntu 20.04 distribution. Not only is it considered to be among the most stable and reliable operating systems, it is also backed by the considerable resources and technical skills of Canonical, Ltd. Ubuntu 20.04 Essentials is designed to provide detailed information on the installation, use and administration of the Ubuntu 20.04 distribution. For beginners, the book covers topics such as operating system installation, the basics of the GNOME desktop environment, configuring email and web servers and installing packages and system updates. Additional installation topics such as dual booting with Microsoft Windows are also covered, together with all important security topics such as configuring a firewall and user and group administration. For the experienced user, topics such as remote desktop access, the Cockpit web interface, logical volume management (LVM), disk partitioning, swap management, KVM virtualization, Secure Shell (SSH), Linux Containers and file sharing using both Samba and NFS are covered in detail to provide a thorough overview of this enterprise class operating system.

The official "Ubuntu 11.04 Unity Desktop Guide" contains information on how to using Ubuntu in a desktop environment (Unity desktop).

The official "Ubuntu 10.10 Installation Guide" contains installation instructions for the Ubuntu 10.10 system (codename "Maverick Meerkat").

Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models. As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed. This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.