OPNsense Beginner to Professional
OPNsense is one of the most powerful open source firewalls and routing platforms available. With OPNsense, you can now protect networks using features that were only previously available to closed source commercial firewalls.
This book is a practical guide to building a comprehensive network defense strategy using OPNsense. You'll start with the basics, understanding how to install, configure, and protect network resources using native features and additional OPNsense plugins. Next, you'll explore real-world examples to gain in-depth knowledge of firewalls and network defense. You'll then focus on boosting your network defense, preventing cyber threats, and improving your knowledge of firewalling using this open source security platform.
By the end of this OPNsense book, you'll be able to install, configure, and manage the OPNsense firewall by making the most of its features. ...
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 ...
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 qualit ...
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 boun ...
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 f ...
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 netwo ...
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 ...
Pro Active Directory Certificate Services
In order to deploy and use Microsoft Certificate Services, you need to understand the fundamentals of cryptography, digital signatures, encryption, TLS, and S/MIME. It is also important to understand the concepts behind public key infrastructure (PKI). This book teaches you all the required background knowledge you need. Then it takes you deeper, step by step, teaching you how to deploy Certificate Services and configure it to issue various digital certificate types, complete with examples of using these certificates with IIS, Outlook, and Windows.
Microsoft-based networks - on-premises, hybrid, and cloud-based networks - are used in companies of all sizes. Within them, there are many applications of digital certificates that can be created and managed by Microsoft Certificate Services. As security is more important than ever, and cryptography and PKI are fundamental to so many of these defenses, understanding Microsoft Certificate Services is becoming an increasingly more desirable ...
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 vi ...
Transformers for Natural Language Processing, 2nd Edition
Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence.
Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers.
An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP.
This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers ...
Math for Deep Learning
Deep learning is everywhere, making this powerful driver of AI something more STEM professionals need to know. Learning which library commands to use is one thing, but to truly understand the discipline, you need to grasp the mathematical concepts that make it tick. This book will give you a working knowledge of topics in probability, statistics, linear algebra, and differential calculus - the essential math needed to make deep learning comprehensible, which is key to practicing it successfully.
Each of the four subfields are contextualized with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. Chapters build upon one another, with foundational topics such as Bayes' theorem followed by more advanced concepts, like training neural networks using vectors, matrices, and derivatives of functions. You'll ultimately put all this math to use as you explore and implement deep learning algorithms, including backp ...