Computer Vision Using Deep Learning
Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human interven ...
Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human interven ...
Linux Cookbook, 2nd Edition
This handy cookbook teaches new-to-intermediate Linux users the essential skills necessary to manage a Linux system, using both graphical and command-line tools. Whether you run Linux in embedded, desktop, server, or cloud or virtual environments, the fundamental skills are the same. This book aims to get you up and running quickly, with copy-paste examples. Carla Schroder provides recipes that cover specific problems, with discussions that explain how each recipe works, as well as references for additional study. You'll learn how to: Use systemd, the new comprehensive service manager; Build simple or complex firewalls with firewalld; Set up secure network connections for Linux systems and mobile devices; Rescue nonbooting systems; Reset lost passwords on Linux and Windows; Use dnsmasq to simplify managing your LAN name services; Manage users and groups and control access to files; Probe your computer hardware and monitor hardware health; Manage the GRUB bootloader and multiboot ...
This handy cookbook teaches new-to-intermediate Linux users the essential skills necessary to manage a Linux system, using both graphical and command-line tools. Whether you run Linux in embedded, desktop, server, or cloud or virtual environments, the fundamental skills are the same. This book aims to get you up and running quickly, with copy-paste examples. Carla Schroder provides recipes that cover specific problems, with discussions that explain how each recipe works, as well as references for additional study. You'll learn how to: Use systemd, the new comprehensive service manager; Build simple or complex firewalls with firewalld; Set up secure network connections for Linux systems and mobile devices; Rescue nonbooting systems; Reset lost passwords on Linux and Windows; Use dnsmasq to simplify managing your LAN name services; Manage users and groups and control access to files; Probe your computer hardware and monitor hardware health; Manage the GRUB bootloader and multiboot ...
Mastering the Lightning Network
The Lightning Network (LN) is a rapidly growing second-layer payment protocol that works on top of Bitcoin to provide near-instantaneous transactions between two parties. With this practical guide, authors Andreas M. Antonopoulos, Olaoluwa Osuntokun, and Rene Pickhardt explain how this advancement will enable the next level of scale for Bitcoin, increasing speed and privacy while reducing fees. Ideal for developers, systems architects, investors, and entrepreneurs looking to gain a better understanding of LN, this book demonstrates why experts consider LN a critical solution to Bitcoin's scalability problem. You'll learn how LN has the potential to support far more transactions than today's financial networks. This book examines: How the Lightning Network addresses the challenge of blockchain scaling; The Basis of Lightning Technology (BOLT) standards documents; The five layers of the Lightning Network Protocol Suite; LN basics, including wallets, nodes, and how to operate one; L ...
The Lightning Network (LN) is a rapidly growing second-layer payment protocol that works on top of Bitcoin to provide near-instantaneous transactions between two parties. With this practical guide, authors Andreas M. Antonopoulos, Olaoluwa Osuntokun, and Rene Pickhardt explain how this advancement will enable the next level of scale for Bitcoin, increasing speed and privacy while reducing fees. Ideal for developers, systems architects, investors, and entrepreneurs looking to gain a better understanding of LN, this book demonstrates why experts consider LN a critical solution to Bitcoin's scalability problem. You'll learn how LN has the potential to support far more transactions than today's financial networks. This book examines: How the Lightning Network addresses the challenge of blockchain scaling; The Basis of Lightning Technology (BOLT) standards documents; The five layers of the Lightning Network Protocol Suite; LN basics, including wallets, nodes, and how to operate one; L ...
Artificial Neural Networks with Java, 2nd Edition
Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example. The book covers the internals of front and back propagation and helps you understand the main principles of neural network processing. You also will learn how to prepare the data to be used in neural network development and you will be able to suggest various techniques of data preparation for many unconventional tasks. This book discusses the practical aspects of using Java for neural network processing. You will know how to use the Encog Java framework for processing large-scale neural network applications. Also covered is the use of neural networks for approximation of non-continuous functions. In addition to using neural networks for regression, this second edition shows you how to use neural networks for computer vision. It focuses on image recognition such as the ...
Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example. The book covers the internals of front and back propagation and helps you understand the main principles of neural network processing. You also will learn how to prepare the data to be used in neural network development and you will be able to suggest various techniques of data preparation for many unconventional tasks. This book discusses the practical aspects of using Java for neural network processing. You will know how to use the Encog Java framework for processing large-scale neural network applications. Also covered is the use of neural networks for approximation of non-continuous functions. In addition to using neural networks for regression, this second edition shows you how to use neural networks for computer vision. It focuses on image recognition such as the ...
Practical Go
Google announced the Go programming language to the public in 2009, with the version 1.0 release announced in 2012. Since its announcement to the community, and the compatibility promise of the 1.0 release, the Go language has been used to write scalable and high-impact software programs ranging from command-line applications and critical infrastructure tools to large-scale distributed systems. It's speed, simplicity, and reliability make it a perfect choice for developers working in various domains. In Practical Go - Building Scalable Network + Non-Network Applications, you will learn to use the Go programming language to build robust, production-ready software applications. You will learn just enough to building command line tools and applications communicating over HTTP and gRPC. This practical guide will cover: Writing command line applications; Writing a HTTP services and clients; Writing RPC services and clients using gRPC; Writing middleware for network clients and servers ...
Google announced the Go programming language to the public in 2009, with the version 1.0 release announced in 2012. Since its announcement to the community, and the compatibility promise of the 1.0 release, the Go language has been used to write scalable and high-impact software programs ranging from command-line applications and critical infrastructure tools to large-scale distributed systems. It's speed, simplicity, and reliability make it a perfect choice for developers working in various domains. In Practical Go - Building Scalable Network + Non-Network Applications, you will learn to use the Go programming language to build robust, production-ready software applications. You will learn just enough to building command line tools and applications communicating over HTTP and gRPC. This practical guide will cover: Writing command line applications; Writing a HTTP services and clients; Writing RPC services and clients using gRPC; Writing middleware for network clients and servers ...
Automated Deep Learning Using Neural Network Intelligence
Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design ...
Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design ...
Network Programming with Go Language, 2nd Edition
Dive into key topics in network architecture implemented with the Google-backed open source Go programming language. Networking topics such as data serialization, application level protocols, character sets and encodings are discussed and demonstrated in Go. This book has been updated to the Go version 1.18 which includes modules, generics, and fuzzing along with updated and additional examples. Beyond the fundamentals, Network Programming with Go, Second Edition covers key networking and security issues such as HTTP protocol changes, validation and templates, remote procedure call (RPC) and REST comparison, and more. Additionally, authors Ronald Petty and Jan Newmarch guide you in building and connecting to a complete web server based on Go. Along the way, use of a Go web toolkit (Gorilla) will be employed. This book can serve as both an essential learning guide and reference on networking concepts and implementation in Go. Free source code is available on Github for this book u ...
Dive into key topics in network architecture implemented with the Google-backed open source Go programming language. Networking topics such as data serialization, application level protocols, character sets and encodings are discussed and demonstrated in Go. This book has been updated to the Go version 1.18 which includes modules, generics, and fuzzing along with updated and additional examples. Beyond the fundamentals, Network Programming with Go, Second Edition covers key networking and security issues such as HTTP protocol changes, validation and templates, remote procedure call (RPC) and REST comparison, and more. Additionally, authors Ronald Petty and Jan Newmarch guide you in building and connecting to a complete web server based on Go. Along the way, use of a Go web toolkit (Gorilla) will be employed. This book can serve as both an essential learning guide and reference on networking concepts and implementation in Go. Free source code is available on Github for this book u ...
A Network Architect's Guide to 5G
As 5G transforms mobile usage and services, network professionals will need to significantly evolve their transport network architectures towards greater sophistication and stronger integration with radio networks, and facilitate transition towards cloud-native 5G mobile core. Until now, however, most 5G guides have foregrounded RF/radio and mobile core innovations, not its implications for data networks. A Network Architects Guide to 5G fills the gap, giving network architects, designers, and engineers essential knowledge for designing and planning their own 5G networks. Drawing on decades of experience with global service providers and enterprise networks, the authors illuminate new and evolving network technologies necessary for building 5G-capable networks, such as segment routing, network slicing, timing and synchronization, edge computing, distributed data centers, integration with public cloud, and more. They explain how 5G blurs boundaries between mobile core, radio access, ...
As 5G transforms mobile usage and services, network professionals will need to significantly evolve their transport network architectures towards greater sophistication and stronger integration with radio networks, and facilitate transition towards cloud-native 5G mobile core. Until now, however, most 5G guides have foregrounded RF/radio and mobile core innovations, not its implications for data networks. A Network Architects Guide to 5G fills the gap, giving network architects, designers, and engineers essential knowledge for designing and planning their own 5G networks. Drawing on decades of experience with global service providers and enterprise networks, the authors illuminate new and evolving network technologies necessary for building 5G-capable networks, such as segment routing, network slicing, timing and synchronization, edge computing, distributed data centers, integration with public cloud, and more. They explain how 5G blurs boundaries between mobile core, radio access, ...
Wireshark Fundamentals
Understand the fundamentals of the Wireshark tool that is key for network engineers and network security analysts. This book explains how the Wireshark tool can be used to analyze network traffic and teaches you network protocols and features. Author Vinit Jain walks you through the use of Wireshark to analyze network traffic by expanding each section of a header and examining its value. Performing packet capture and analyzing network traffic can be a complex, time-consuming, and tedious task. With the help of this book, you will use the Wireshark tool to its full potential. You will be able to build a strong foundation and know how Layer 2, 3, and 4 traffic behave, how various routing protocols and the Overlay Protocol function, and you will become familiar with their packet structure. Troubleshooting engineers will learn how to analyze traffic and identify issues in the network related to packet loss, bursty traffic, voice quality issues, etc. The book will help you understand ...
Understand the fundamentals of the Wireshark tool that is key for network engineers and network security analysts. This book explains how the Wireshark tool can be used to analyze network traffic and teaches you network protocols and features. Author Vinit Jain walks you through the use of Wireshark to analyze network traffic by expanding each section of a header and examining its value. Performing packet capture and analyzing network traffic can be a complex, time-consuming, and tedious task. With the help of this book, you will use the Wireshark tool to its full potential. You will be able to build a strong foundation and know how Layer 2, 3, and 4 traffic behave, how various routing protocols and the Overlay Protocol function, and you will become familiar with their packet structure. Troubleshooting engineers will learn how to analyze traffic and identify issues in the network related to packet loss, bursty traffic, voice quality issues, etc. The book will help you understand ...
Hands-on Machine Learning with Python
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical le ...
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical le ...