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## Network Marketing. O Negócio do Século XXI seleção on-line livro

- Autor: Demétrio Melo
- Editor: Alta Books
- Data de publicação: 2014-10-01
- ISBN: 9788576088905
- Número de páginas: 237 pages
- Tag: network, marketing, negocio, seculo

Nunca na história do network marketing discutiu-se tanto acerca desta indústria como nos dias de hoje. Estranhamente, porém, a incompreensão, os preconceitos e a confusão entre o ético e o antiético, moral e amoral, legal e ilegal ainda permanecem como foco de muitas controvérsias. Mas então, o que é network marketing? O que o diferencia de uma pirâmide financeira? Quais os principais passos a serem tomados neste negócio? Estas e outras questões são analisadas de forma clara e dinâmica em Network Marketing - O Negócio do Século XXI. O livro traz uma inovadora visão acerca das novas perspectivas deste negócio na Era da Informação, esclarecendo todas as suas dúvidas.

## Networks seleção on-line livro

- Autor: Mark Newman
- Editor: OUP Oxford
- Data de publicação: 2018-07-26
- ISBN: 0198805098
- Número de páginas: 800 pages
- Tag: networks

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on an unprecedented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and central developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.

Topics covered include the measurement of networks; methods for analyzing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms; mathematical models of networks, including random graph models and generative models; and theories of dynamical processes taking place on networks.

Topics covered include the measurement of networks; methods for analyzing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms; mathematical models of networks, including random graph models and generative models; and theories of dynamical processes taking place on networks.

## Tyrion Lannister e o Network: 10 Dicas de Como Melhorar o Seu Network seleção on-line livro

- Autor: Rodrigo Selback
- Editor: Blog do Selback
- Data de publicação: 2018-09-13
- Número de páginas: 5 pages
- Tag: tyrion, lannister, network, dicas, melhorar, network

Se tem alguém na cultura pop que pode nos inspirar e nos ensinar a fazer Network esse alguém é o Tyrion Lannister.

Na série Guerra dos Tronos ele profere uma frase que é um verdadeiro mantra do network “tento conhecer tantas pessoas quanto posso, porque nunca se sabe de qual delas irá precisar”

Na série Guerra dos Tronos ele profere uma frase que é um verdadeiro mantra do network “tento conhecer tantas pessoas quanto posso, porque nunca se sabe de qual delas irá precisar”

## Neural Networks and Deep Learning: A Textbook seleção on-line livro

- Autor: Charu C. Aggarwal
- Editor: Springer
- Data de publicação: 2018-09-30
- ISBN: 3319944622
- Número de páginas: 524 pages
- Tag: neural, networks, learning, 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. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:

**The basics of neural networks: ** Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship 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. These methods are studied together with recent feature engineering methods like word2vec.

**Fundamentals of neural networks:** A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.

**Advanced topics in neural networks: **Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.

The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

## Network Programmability and Automation seleção on-line livro

- Autor: Jason Edelman
- Editor: O′Reilly
- Data de publicação: 2018-03-09
- ISBN: 9781491931257
- Número de páginas: 580 pages
- Tag: network, programmability, automation

Automation is the new skillset that network engineers need to pick up. Much like sysadmins have had to learn how to use new tools like Chef and Puppet, network engineers are learning that they just can't do things manually anymore. With examples in each chapter, this practical book provides you with baseline skills in network programmability and automation, using a range of technologies including Linux, Python, JSON, and XML. No previous knowledge of software development, programming, automation, or DevOps is required.

- Understand the basics of Linux as applied to networking
- Learn how to use text editors and Python to automate networks
- Apply sound software design principles like continuous integration, DevOps, source control, etc. to optimize networks

## The Network (English Edition) seleção on-line livro

- Autor: Karen Sekali
- Data de publicação: 2018-08-24
- Número de páginas: 43 pages
- Tag: network, english, edition

Lucas Hunter, a young software developer working in a tech company awakens into the future as soon as he falls asleep, there he finds out that the world's future doesn't look so bright and he is the only one that can save it, he is supposed to do it by transferring the technology from the year where he awakens to the year from where he comes. Dr. Ethan Woods and his team (characters from the future) are helping him to learn about the technology and successfully transfer it. Things get even more interesting when Dr. Ethan meets Lucas on his presentation of the "Zepto Chip" that Lucas invented back in the time where Lucas lives...

## The Network Imperative: How to Survive and Grow in the Age of Digital Business Models seleção on-line livro

- Autor: Barry Libert
- Editor: Harvard Business Review Press
- Data de publicação: 2016-06-28
- ISBN: 1633692051
- Número de páginas: 256 pages
- Tag: network, imperative, survive, digital, business, models

Digital networks are changing all the rules of business. New, scalable, digitally networked business models, like those of Amazon, Google, Uber, and Airbnb, are affecting growth, scale, and profit potential for companies in every industry. But this seismic shift isn’t unique to digital start-ups and tech superstars. Digital transformation is affecting every business sector, and as investor capital, top talent, and customers shift toward network-centric organizations, the performance gap between early and late adopters is widening.

So the question isn’t whether your organization needs to change, but when and how much.

Supported by research that covers fifteen hundred companies, authors Barry Libert, Megan Beck, and Jerry Wind guide leaders and investors through the ten principles that all organizations can use to grow and profit regardless of their industry. They also share a five-step process for pivoting an organization toward a more scalable and profitable business model.

So the question isn’t whether your organization needs to change, but when and how much.

*The Network Imperative*is a call to action for managers and executives to embrace network-based business models. The benefits are indisputable: companies that leverage digital platforms to co-create and share value with networks of employees, customers, and suppliers are fast outpacing the market. These companies, or*network orchestrators*, grow faster, scale with lower marginal cost, and generate the highest revenue multipliers.Supported by research that covers fifteen hundred companies, authors Barry Libert, Megan Beck, and Jerry Wind guide leaders and investors through the ten principles that all organizations can use to grow and profit regardless of their industry. They also share a five-step process for pivoting an organization toward a more scalable and profitable business model.

*The Network Imperative*, brimming with compelling case studies and actionable advice, provides managers with what they really need: new tools and frameworks to generate unprecedented value in a rapidly changing age.## DEEP LEARNING using MATLAB. NEURAL NETWORK APPLICATIONS (English Edition) seleção on-line livro

- Autor: K. Taylor
- Data de publicação: 2017-02-15
- Número de páginas: 335 pages
- Tag: learning, using, matlab, neural, network, applications, english, edition

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.

The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.

The more important features are the following:

•Deep learning, including convolutional neural networks and autoencoders

•Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox)

•Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN)

•Unsupervised learning algorithms, including self-organizing maps and competitive layers

•Apps for data-fitting, pattern recognition, and clustering

•Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance

•Simulink® blocks for building and evaluating neural networks and for control systems applications

This book develops deep learning, including convolutional neural networks and autoencoders and other types of advanced neural networks

Deep learning is part of a broader family of machine learning methods based on learning representations of data. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.

The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.

The more important features are the following:

•Deep learning, including convolutional neural networks and autoencoders

•Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox)

•Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN)

•Unsupervised learning algorithms, including self-organizing maps and competitive layers

•Apps for data-fitting, pattern recognition, and clustering

•Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance

•Simulink® blocks for building and evaluating neural networks and for control systems applications

This book develops deep learning, including convolutional neural networks and autoencoders and other types of advanced neural networks

## Machine Learning for Beginners: Your Ultimate Guide To Machine Learning For Absolute Beginners, Neural Networks, Scikit-Learn, Deep Learning, TensorFlow, ... Python, Data Science (English Edition) seleção on-line livro

- Autor: Jonathan S. Walker
- Data de publicação: 2018-09-08
- Número de páginas: 70 pages
- Tag: machine, learning, beginners, ultimate, guide, machine, learning, absolute, beginners, neural, networks, scikit, learn, learning, tensorflow, python, science, english, edition

Machine learning is going to be something that you are going to use so that you can discover why and how you are getting the outcomes that you are getting with the program that you are using.

With machine learning, you are going to have the option of putting the data in that you want into the program and getting the results that you want to get. You are going to better understand where you made a mistake so that you can go back in and fix it.

While you are going to get frustrated with machine learning, it is going to help you in the long run.

It is an innovative idea that you understand how to use Python so that you can better use machine learning for what you need it to be used for. If you do not know how to script with python, you are going to want to learn the basics of Python just so that you know how to navigate python.

With machine learning, you are going to have the option of putting the data in that you want into the program and getting the results that you want to get. You are going to better understand where you made a mistake so that you can go back in and fix it.

While you are going to get frustrated with machine learning, it is going to help you in the long run.

It is an innovative idea that you understand how to use Python so that you can better use machine learning for what you need it to be used for. If you do not know how to script with python, you are going to want to learn the basics of Python just so that you know how to navigate python.

## Make Your Own Neural Network (English Edition) seleção on-line livro

- Autor: Tariq Rashid
- Data de publicação: 2016-04-16
- Tag: neural, network, english, edition

A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language.

Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.

This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included.

The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already!

You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks.

Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples.

Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals.

Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi.

All the code in this has been tested to work on a Raspberry Pi Zero.

Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.

This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included.

The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already!

You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks.

Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples.

Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals.

Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi.

All the code in this has been tested to work on a Raspberry Pi Zero.