Products related to Networks:
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Programming Machine Learning : From Coding to Deep Learning
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool.But where to start? It's easy to be intimidated, even as a software developer.The good news is that it doesn't have to be that hard.Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system.Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty.Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning.Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work.Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on.Iterate on your design, and add layers of complexity as you go.Build an image recognition application from scratch with supervised learning.Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning.Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets.Train and refine those networks with backpropagation and batching.Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system.Start from the beginning and code your way to machine learning mastery.What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly.Apart from that, you'll only need your computer, and your code-adept brain.
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Software Defined Networks : A Comprehensive Approach
Software Defined Networks: A Comprehensive Approach, Second Edition provides in-depth coverage of the technologies collectively known as Software Defined Networking (SDN).The book shows how to explain to business decision-makers the benefits and risks in shifting parts of a network to the SDN model, when to integrate SDN technologies in a network, and how to develop or acquire SDN applications. In addition, the book emphasizes the parts of the technology that encourage opening up the network, providing treatment for alternative approaches to SDN that expand the definition of SDN as networking vendors adopt traits of SDN to their existing solutions. Since the first edition was published, the SDN market has matured, and is being gradually integrated and morphed into something more compatible with mainstream networking vendors.This book reflects these changes, with coverage of the OpenDaylight controller and its support for multiple southbound protocols, the Inclusion of NETCONF in discussions on controllers and devices, expanded coverage of NFV, and updated coverage of the latest approved version (1.5.1) of the OpenFlow specification.
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Super User Networks for Software Projects : Best practices for training and change management
To successfully compete in the modern world, organisations are accelerating their digital transformations.It is essential for them to utilise best practice methods when implementing new systems and adopting new technologies.The Super User Model is a proven methodology for delivering digital transformation projects, including large scale transitions. This book is designed to help project, learning and change professionals who are responsible for this delivery by introducing the Super User Model and how to use it.It provides the tools, techniques, and theory needed to plan out where and how Super Users can add value at each phase of a project - across software design, change management, testing, and training - and to manage Super User Networks beyond the transition into business as usual for sustained benefits. If your organisation is investing in new technology, you cannot afford not to use Super Users in taking the latest step in your digital transformation journey.
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Time Based Development Planning of Distribution Networks
In the book, a time-based model for distribution network development planning is presented, considering the possibility of using distributed energy generation technologies and the existence of capacitor banks.The proposed model specifies the appropriate location, capacity, and time for the use of distributed generation technologies and capacitor banks, as well as the schedule for increasing the capacity of network lines.To solve the problem, a genetically enhanced algorithm is used to optimize the network development plan, including the time, place, and capacity of DG and capacitor banks in the distribution network, as well as to optimize investment costs and operating costs.Also, in order to validate and evaluate the efficiency of the proposed solution to solve the distribution network development planning problem on a 17-bus radial distribution network, it was implemented in the MATLAB programming environment in a computer system.
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Is learning programming and software development very challenging?
Learning programming and software development can be challenging for some people, as it requires logical thinking, problem-solving skills, and attention to detail. However, with dedication, practice, and the right resources, it is definitely achievable. Breaking down complex concepts into smaller, more manageable parts and seeking help from online tutorials, courses, and communities can make the learning process easier and more enjoyable. Ultimately, the level of challenge will vary depending on the individual's background, experience, and learning style.
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Which programming language is best suited for programming neural networks?
Python is widely considered the best programming language for programming neural networks. It has a rich ecosystem of libraries and frameworks specifically designed for machine learning and neural network development, such as TensorFlow, Keras, and PyTorch. Python's simplicity, readability, and flexibility make it an ideal choice for implementing complex neural network algorithms and models. Additionally, Python has a large and active community, providing ample resources and support for developers working on neural network projects.
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How can I use two networks simultaneously with a software?
To use two networks simultaneously with software, you can utilize a feature called network bonding or network teaming. This feature allows you to combine the bandwidth of multiple network interfaces, effectively using them as one connection. You can configure network bonding in your operating system settings or use third-party software that supports this functionality. By setting up network bonding, you can improve network performance, increase reliability, and achieve load balancing across the two networks.
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How can I use 2 networks simultaneously with a software?
You can use two networks simultaneously by using a software called a network load balancer or a network bonding software. These software solutions allow you to combine the bandwidth of multiple network connections, enabling faster data transfer speeds and increased reliability. By configuring the software to use both networks, you can distribute network traffic across the connections, optimizing performance and ensuring a more stable internet connection.
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Neural Networks and Deep Learning : A Textbook
This book covers both classical and modern models in deep learning.The primary focus is on the theory and algorithms of deep learning.The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications.Why do neural networks work? When do they work better than off-the-shelf machine-learning models?When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems.Deep learning methods for various data domains, such as text, images, and graphs are presented in detail.The chapters of this book span three categories: The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2. Many traditional machine learning models can be understood as special cases of neural networks.Chapter 3 explores the connections between traditional machine learning and neural networks.Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5.Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks.Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The textbook is written for graduate students and upper under graduate level students.Researchers and practitioners working within this related field will want to purchase this as well. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks.Many chapters have been significantly revised over the first edition. Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.
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Learning, Training, and Development in Organizations
This scholarly book in SIOP’s Organizational Frontier series looks at research on enhancing knowledge acquisition and its application in organizations.It concentrates on training, design and delivery given the changing nature of work and organizations.Now that work is increasingly complex, there is greater emphasis on expertise and cognitive skills.Advances in technology such as computer simulations and web-based training are necessitating a more active role for the learner in the training process.In the broad context of the organization systems, this book promotes learning and development as a continuous lifelong endeavor.
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Effective Software Development for the Enterprise : Beyond Domain Driven Design, Software Architecture, and Extreme Programming
It’s a jungle out there. Delivering software solutions with predictable costs, time, scope, and quality has become more complicated than ever.Developers need to account for multiple moving targets, including often avoided but always relevant factors such as culture, people, and leadership.To effectively deal with these challenges, it’s necessary to rethink the entire delivery chain of modern software development.This book addresses all these topics, putting everything in perspective, from the potential awkwardness created by cultural differences to connecting code with business outcomes to make delivering quality solutions possible.As a software engineer who strives to be his best and challenge the status quo when building IT systems, author Tengiz Tutisani always wanted a book that would cover all of these elements from a developer’s perspective — so he wrote one.Effective Software Development for the Enterprise builds on advanced disciplines, methodologies, and techniques derived from domain-driven design, software architecture, and extreme programming.Its objective is to aid modern software engineers and solution architects in building and delivering high-quality solutions for an enterprise while meeting ambitious criteria:Meet users' expectationsDeliver solutions on time with no defectsCreate products that can scale-out horizontallySolutions should not require a dedicated production support teamAccelerate development paceDouble ROI per developer, team, and softwareYou will learn how to achieve these objectives in any engineering environment by developing solid strategies based on the industry's well-known, recognized, and proven patterns and principles.After reading this book, you will be ready to effectively deal with any and all challenges posed by today’s hyper-competitive, globally-diverse, fast-moving enterprise environment. Who This Book Is ForProfessional software engineers and solution architects, as well as those aspiring to become one.Additionally, software engineering leaders will benefit from reading it, as it will help them maximize their impact via successful software project deliveries.
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Social Networks in Language Learning and Language Teaching
Sociocultural research has long recognized the necessity of sustained interpersonal interaction for language development.However, less is known about the underlying relationships that promote language acquisition and their relevance for language classrooms.Presenting cutting-edge research on social networks and their applications in language teaching, this book explores the relationships that mediate language learning in and out of classrooms. Highlighting the complexity of language in multilingual contexts, chapters engage social network analysis to understand the role of instructional practices, socialization, motivation, language status, online communications technology, and language policies in the development of social resources for language learning.Discussing popular language teaching frameworks such as translanguaging, Social Networks in Language Learning and Language Teaching provides a nuanced account of the influences of social context on language learning, exploring classroom applications and pointing the way to a robust research agenda.
Price: 29.99 £ | Shipping*: 0.00 £
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Which programming languages are used in software development?
There are many programming languages used in software development, including popular languages such as Java, Python, C++, JavaScript, and Ruby. Each language has its own strengths and is used for different purposes in software development. For example, Java is commonly used for building enterprise-level applications, while Python is known for its simplicity and versatility. C++ is often used for system software and game development, while JavaScript is essential for web development. Overall, the choice of programming language depends on the specific requirements of the software being developed.
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What are computer networks?
Computer networks are systems that allow multiple computers to communicate and share resources with each other. They can be as simple as two computers connected together or as complex as a global network like the internet. Networks can be wired or wireless and are essential for enabling communication, data sharing, and collaboration between devices. They play a crucial role in modern society by facilitating the exchange of information and enabling various services and applications to function.
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Are social networks good?
Social networks can have both positive and negative effects. On one hand, they allow people to connect with others, share information, and stay updated on current events. They also provide a platform for businesses to reach a wider audience. However, social networks can also contribute to issues such as cyberbullying, privacy concerns, and addiction to technology. Ultimately, the impact of social networks depends on how they are used and the individual's ability to manage their online interactions.
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What are social networks?
Social networks are online platforms that allow individuals to connect and interact with others, share information, and build relationships. These platforms enable users to create profiles, share updates, photos, and videos, and communicate with others through messaging and comments. Social networks have become an integral part of modern society, providing a virtual space for people to stay connected, collaborate, and engage with a wider community.
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