Automated Deep Learning Using Neural Network Intelligence, Gridin
Автор: Goldberg Yoav Название: Neural Network Methods in Natural Language Processing ISBN: 1627052984 ISBN-13(EAN): 9781627052986 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 11504.00 р. Наличие на складе: Нет в наличии.
Описание: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Описание: What if you could teach your computer how to learn the way the human brain does?And what if you could do that even without having any background in programming?
If you think that this is something that may have a huge impact on your life please keep reading, because you are right... it is
If you are reading this you probably already know something about Deep Learning. You probably know that this is maybe the number one secret behind the success of the big ones, like Google, Facebook and Amazon. Maybe you are also aware that it has been crucial in the tremendous growth of the greatest startups of the last decade, like Airbnb, Uber or Spotify, just to name some.
So, you know what we are talking about, still, you will probably have some questions too, like...
Is this for me?
Is this something I can learn?
And once I have learned it, can I also use it in everyday business or it concerns only the big ones?
Well, the answer is YES
YES, this is for you (if you want to)
YES, you can learn it (if you commit to)
YES, you can use it for your own business (but it can also open you many doors in finding a great job)
So, either if you want to apply Artificial Intelligence to your own startup, or use it to grow your current business to the next level, or just to find a great job based on your skills and passion, Deep Learning is a great point to start.
With Deep Learning for Beginners you will learn:
The most effective starting points when training deep neural nets
How to talk with deep neural networks
What libraries are and which one is the best for you
Why a decision tree is the smartest way to go
The TensorFlow parts that are going to make your coding life easy
If you don't know anything about programming, understanding Deep Learning is the ideal place to start. Still, if you already know something about programming but not about how to apply it to Artificial Intelligence, Deep Learning is what you want to understand.
Buy now Deep Learning for Beginners to start your path of Artificial Intelligence.
Описание: What if you could teach your computer how to learn the way the human brain does?And what if you could do that even without having any background in programming?
If you think that this is something that may have a huge impact on your life please keep reading, because you are right... it is
If you are reading this you probably already know something about Deep Learning. You probably know that this is maybe the number one secret behind the success of the big ones, like Google, Facebook and Amazon. Maybe you are also aware that it has been crucial in the tremendous growth of the greatest startups of the last decade, like Airbnb, Uber or Spotify, just to name some.
So, you know what we are talking about, still, you will probably have some questions too, like...
Is this for me?
Is this something I can learn?
And once I have learned it, can I also use it in everyday business or it concerns only the big ones?
Well, the answer is YES
YES, this is for you (if you want to)
YES, you can learn it (if you commit to)
YES, you can use it for your own business (but it can also open you many doors in finding a great job)
So, either if you want to apply Artificial Intelligence to your own startup, or use it to grow your current business to the next level, or just to find a great job based on your skills and passion, Deep Learning is a great point to start.
With Deep Learning for Beginners you will learn:
The most effective starting points when training deep neural nets
How to talk with deep neural networks
What libraries are and which one is the best for you
Why a decision tree is the smartest way to go
The TensorFlow parts that are going to make your coding life easy
If you don't know anything about programming, understanding Deep Learning is the ideal place to start. Still, if you already know something about programming but not about how to apply it to Artificial Intelligence, Deep Learning is what you want to understand.
Buy now Deep Learning for Beginners to start your path of Artificial Intelligence.
Автор: Purkait Niloy Название: Hands-On Neural Networks with Keras ISBN: 1789536081 ISBN-13(EAN): 9781789536089 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will intuitively build on the fundamentals of neural networks, deep learning and thoughtfully guide the readers through real-world use cases. You will learn to implement neural networks as well as how to develop and embed intelligence in products and services using the latest open source and industry level tools available in the market.
Автор: by Shashi Narayan, Claire Gardent Название: Deep Learning Approaches to Text Production ISBN: 1681737604 ISBN-13(EAN): 9781681737607 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 14276.00 р. Наличие на складе: Нет в наличии.
Описание: Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular, we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text, how encoders learn to produce better input representations, and how neural generators account for task-specific objectives. Indeed, each text-production task raises a slightly different challenge (e.g, how to take the dialogue context into account when producing a dialogue turn, how to detect and merge relevant information when summarising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally, this book considers text-to-text, meaning-to-text, and data-to-text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers, graduate students, and industrials interested in text production from different forms of inputs.
Автор: Stamp Mark, Alazab Mamoun, Shalaginov Andrii Название: Malware Analysis Using Artificial Intelligence and Deep Learning ISBN: 3030625818 ISBN-13(EAN): 9783030625818 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis.
Автор: Quang Hung Do Название: Artificial Neural Network Applications in Business and Engineering ISBN: 1799832384 ISBN-13(EAN): 9781799832386 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 39085.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method.
Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.
Автор: Quang Hung Do Название: Artificial Neural Network Applications in Business and Engineering ISBN: 1799832392 ISBN-13(EAN): 9781799832393 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 32155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method.
Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.
Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao Название: Network Embedding: Theories, Methods, and Applications ISBN: 1636390463 ISBN-13(EAN): 9781636390468 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 15939.00 р. Наличие на складе: Нет в наличии.
Описание:
Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.
This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao Название: Network Embedding: Theories, Methods, and Applications ISBN: 1636390447 ISBN-13(EAN): 9781636390444 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12751.00 р. Наличие на складе: Нет в наличии.
Описание:
Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.
This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
Автор: Shanmuganathan Subana, Samarasinghe Sandhya Название: Artificial Neural Network Modelling ISBN: 3319803638 ISBN-13(EAN): 9783319803630 Издательство: Springer Рейтинг: Цена: 22451.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Artificial Neural Networks Applications: An Introduction.- Order in the Black Box: Consistency And Robustness Of Neuron Activation Of Feed Forward Neural Networks And Its Use In Efficient Optimization Of Network Structure.- Artificial Neural Networks as Models of Robustness in Development And Regeneration: Stability of Memory During Morphological Remodeling.- A Structure Optimization Algorithm of Neural Networks For Pattern Learning from Educational Data.- Stochastic Neural Networks for Modelling Random Processes from Observed Data.- Curvelet Interaction with Artificial Neural Networks.- Hybrid Wavelet Neural Network Approaches.-Quantification of Prediction Uncertainty in Artificial Neural Network Models.- Classifying Calpain Inhibitors for The Treatment of Cataracts: A Self Organising Map (SOM) ANN/KM Approach in Drug Discovery.- Improved Ultrasound Based Computer Aided Diagnosis System for Breast Cancer Using Neural Networks Incorporating a Novel Effective Feature - Degree of Central Regularity of Mass.-SOM Clustering and Modelling of Australian Railway Drivers' Sleep, Wake, Duty Profiles.- A Neural Approach to Electricity Demand Forecasting.- Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia.- Artificial Neural Networks in Precipitation Nowcasting: An Australian Case Study.- Construction of Pmx Concentration Surfaces Using Neural Evolutionary Fuzzy Models of Semi Physical Class.- Application of Artificial Neural Network in Social Media Data Analysis: A Case of Lodging Business in Philadelphia.- Sentiment Analysis on Morphologically Rich Languages - An Artificial Neural Network (ANN) Approach.- Predicting Stock Price Movements with News Sentiment: An Artificial Neural Networks Approach.- Modelling Mode Choice of Individual In Linked Trips with Artificial Neural Networks and Fuzzy Representation.- Artificial Neural Network (ANN) Pricing Model for Natural Rubber Products Based on Climate Dependencies.- A Hybrid Artificial Neural Network (ANN) Approach to Spatial and Non-Spatial Attribute Data Mining: A Case Study Experience.
Описание: Introduction.- Designing of dynamic neural networks.- Estimation methods in training of ANNs for robust fault diagnosis.- MLP in robust fault detection of static non-linear systems.- GMDH networks in robust fault detection of dynamic non-linear systems.- State-space GMDH networks for actuator robust FDI.
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