Verleger: 16
Network Vulnerability Assessment. Identify security loopholes in your network’s infrastructure
Sagar Rahalkar
The tech world has been taken over by digitization to a very large extent, and so it’s become extremely important for an organization to actively design security mechanisms for their network infrastructures. Analyzing vulnerabilities can be one of the best ways to secure your network infrastructure.Network Vulnerability Assessment starts with network security assessment concepts, workflows, and architectures. Then, you will use open source tools to perform both active and passive network scanning. As you make your way through the chapters, you will use these scanning results to analyze and design a threat model for network security. In the concluding chapters, you will dig deeper into concepts such as IP network analysis, Microsoft Services, and mail services. You will also get to grips with various security best practices, which will help you build your network security mechanism.By the end of this book, you will be in a position to build a security framework fit for an organization.
Gordon Davies
A network is a collection of computers, servers, mobile devices, or other computing devices connected for sharing data. This book will help you become well versed in basic networking concepts and prepare to pass Microsoft's MTA Networking Fundamentals Exam 98-366.Following Microsoft's official syllabus, the book starts by covering network infrastructures to help you differentiate intranets, internets, and extranets, and learn about network topologies. You’ll then get up to date with common network hardware devices such as routers and switches and the media types used to connect them together. As you advance, the book will take you through different protocols and services and the requirements to follow a standardized approach to networking. You’ll get to grips with the OSI and TCP/IP models as well as IPv4 and IPv6. The book also shows you how to recall IP addresses through name resolution. Finally, you’ll be able to practice everything you’ve learned and take the exam confidently with the help of mock tests. By the end of this networking book, you’ll have developed a strong foundation in the essential networking concepts needed to pass Exam 98-366.
Manpreet Singh Ghotra, Rajdeep Dua
If you're aware of the buzz surrounding the terms such as machine learning, artificial intelligence, or deep learning, you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that.You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders.By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.
James Loy
Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them.It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch.By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.
V Kishore Ayyadevara
This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data.Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Neural Networks with R. Build smart systems by implementing popular deep learning models in R
Balaji Venkateswaran, Giuseppe Ciaburro
Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you’ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.
Bo Wang, Cristian Mitroi, Feng Wang, Shubham...
Search is a big and ever-growing part of the tech ecosystem. Traditional search, however, has limitations that are hard to overcome because of the way it is designed. Neural search is a novel approach that uses the power of machine learning to retrieve information using vector embeddings as first-class citizens, opening up new possibilities of improving the results obtained through traditional search.Although neural search is a powerful tool, it is new and finetuning it can be tedious as it requires you to understand the several components on which it relies. Jina fills this gap by providing an infrastructure that reduces the time and complexity involved in creating deep learning–powered search engines. This book will enable you to learn the fundamentals of neural networks for neural search, its strengths and weaknesses, as well as how to use Jina to build a search engine. With the help of step-by-step explanations, practical examples, and self-assessment questions, you'll become well-versed with the basics of neural search and core Jina concepts, and learn to apply this knowledge to build your own search engine.By the end of this deep learning book, you'll be able to make the most of Jina's neural search design patterns to build an end-to-end search solution for any modality.
Neurobiologia stawiania granic
Juliane Taylor-Shore
Wykorzystaj neurobiologię, aby skutecznie stawiać granice! Mądre stawianie granic to podstawa higieny psychicznej i rozwoju. Bez nich łatwo ulegamy wypaleniu, a także tracimy pewność siebie, poczucie bezpieczeństwa i kontroli nad życiem, zarówno prywatnym, jak i zawodowym. Z tej książki dowiesz się, jak w sześciu krokach możesz przeprogramować swój mózg, by wreszcie zacząć dostrzegać, gdzie znajdują się twoje indywidualne granice, oraz nauczyć się je wyznaczać. Dzięki temu: nauczysz się reagować w sposób świadomy i zaczniesz panować nad codziennymi wyzwaniami i konfliktami, wzmocnisz poczucie sprawczości we wszystkich obszarach życia, poprawisz swoje relacje z innymi dzięki większej pewności siebie i skutecznej komunikacji, nabierzesz zaufania do siebie i swojej intuicji, zaczniesz żyć w zgodzie z własnymi wartościami, zwiększysz asertywność i zadbasz o swoje potrzeby, staniesz się najlepszą wersją siebie. Autorka Juliane Taylor Shore, terapeutka małżeństw i rodzin z ponaddwudziestoletnim doświadczeniem, przeprowadzi cię przez proces odkrywania nowych sposobów myślenia o sobie, twoich relacjach i emocjach, a także przedstawi ci codzienne praktyki, które pozwolą ci wypracować nowe wzorce zachowań.