3rd International Winter School And Conference On Network Science: Netsci-x 2017

Download 3rd International Winter School And Conference On Network Science: Netsci-x 2017

This book contains original research chapters related to the interdisciplinary field of complex networks spanning biological and environmental networks, social, technological, and economic networks.

Many natural phenomena can be modeled as networks where nodes are the primitive compounds and links represent their interactions, similarities, or distances of sorts. Complex networks have an enormous impact on research in various fields like biology, social sciences, engineering, and cyber-security to name a few. The topology of a network often encompasses important information on the functionality and dynamics of the system or the phenomenon it represents. Network science is an emerging interdisciplinary discipline that provides tools and insights to researchers in a variety of domains.

NetSci-X is the central winter conference within the field and brings together leading researchers and innovators to connect, meet, and establish interdisciplinary channels for collaboration. It is the largest and best known event in the area of network science.

This text demonstrates how ideas formulated by authors with different backgrounds are transformed into models, methods, and algorithms that are used to study complex systems across different domains and will appeal to researchers and students within in the field.

Deep Learning With Keras

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Key Features

Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games

See how various deep-learning models and practical use-cases can be implemented using Keras

A practical, hands-on guide with real-world examples to give you a strong foundation in Keras

Book Description

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer.

Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks.

What you will learn

Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm

Fine-tune a neural network to improve the quality of results

Use deep learning for image and audio processing

Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases

Identify problems for which Recurrent Neural Network (RNN) solutions are suitable

Explore the process required to implement Autoencoders

Evolve a deep neural network using reinforcement learning

About the Author

Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields spanning from publishing (Elsevier) to consumer internet (Ask.com and Tiscali) and high-tech R&D (Microsoft and Google).

Sujit Pal is a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run.

Table of Contents

Neural Networks Foundations

Keras Installation and API

Deep Learning with ConvNets

Generative Adversarial Networks and WaveNet

Word Embeddings

Recurrent Neural Network — RNN

Additional Deep Learning Models

AI Game Playing

Conclusion

Big Data And Neural Networks & Deep Learning: 2 Manuscripts

Download Big Data And Neural Networks & Deep Learning: 2 Manuscripts

Big Data:

The world of business is changing at an ever accelerating rate. Businesses that choose not to adopt the latest technologies are destined to suffer the consequences of obsolescence. The best way for a small to medium sized business to improve profits and secure its future is through adopting data analytics and using big data to improve automation and solve optimization problems.

Hidden within the data that we already collect for our businesses lies solutions to all types of questions. From understanding the best hours for your business to keep, to managing payroll, big data and data analytics can vastly improve the efficiency of any business. This is a concept has been well known to the world’s largest businesses, but for years has been gated behind the cost of expensive software solutions.

Today there are more software solutions for small and medium size businesses than ever before. Whether you want to solve for optimization problems to better understand your business and customer base, or if you merely want to automate ordering items and paying your employees, big data and data analytics is the solution that you need. Continue reading and soon you will have the knowledge to secure your business for long into the future.

Neural Networks:

Your body is made up of a neural networks that helps you to think, do tasks and even breathe. The neural networks that are in a body are very important. But, what if your body isn’t the only place that neural networks can be found?

Artificial neural networks are present in systems of computers that all work together to be able to accomplish various goals. They are useful in mathematics, production and many other instances. The artificial neural networks are a

building block toward making things more lifelike when it comes to computers.

Read on to learn more about how artificial and biological neural networks are similar, what types of neural networks are available for systems of computers and how your computer may one day be able to become self-aware.

Computational Intelligence Applications To Option Pricing, Volatility Forecasting And Value At Risk

Download Computational Intelligence Applications To Option Pricing, Volatility Forecasting And Value At Risk

This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models.