Автор: Chakraborty, Debarati B Название: Granular video computing: with rough sets, deep learning and in iot ISBN: 981122711X ISBN-13(EAN): 9789811227110 Издательство: World Scientific Publishing Рейтинг: Цена: 12672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.
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.
The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.
This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks -- demonstrating how such case studies are designed, executed and how their results are obtained.
The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.
Автор: Caterini Название: Deep Neural Networks in a Mathematical Framework ISBN: 3319753037 ISBN-13(EAN): 9783319753034 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks.
Автор: Karthikrajan Senthilnathan, Balamurugan Shanmugam, Dinesh Goyal, Iyswarya Annapoorani, Ravi Samikannu Название: Deep Learning Applications and Intelligent Decision Making in Engineering ISBN: 1799821099 ISBN-13(EAN): 9781799821090 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 24948.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is designed for engineers, computer scientists, programmers, software engineers, researchers, academics, and students.
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.
Get a head start in the world of AI and deep learning by developing your skills with PyTorch
Key Features
Learn how to define your own network architecture in deep learning
Implement helpful methods to create and train a model using PyTorch syntax
Discover how intelligent applications using features like image recognition and speech recognition really process your data
Book Description
Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you're starting from scratch.
It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures.
The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues.
By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps.
What you will learn
Explore the different applications of deep learning
Understand the PyTorch approach to building neural networks
Create and train your very own perceptron using PyTorch
Solve regression problems using artificial neural networks (ANNs)
Handle computer vision problems with convolutional neural networks (CNNs)
Perform language translation tasks using recurrent neural networks (RNNs)
Who this book is for
This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly.
Описание: This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.
Описание: This Comprehensive Guide Will Help You Take the Headache Out of Python Coding, Programming, and Data Analysis
Python is a very popular language and, if you're just starting with coding, you should make sure that it is the first language to learn. But how do you start learning this language?
The first step in learning any programming language is making sure that you understand how to learn it.
The best way to learn Python is to understand the big picture of all that you need to learn before you dive in and start learning.
Learning Python is easy, even if you've never learned about coding before. It offers excellent readability and simple-to-learn syntax, which helps beginners learn this programming language in no time
This book will not only teach you everything you should know about Python, but it will also give you a greater insight into coding, programming, machine learning, and data analysis.
Here is what this book will teach you:
-The fundamental skills that will bootstrap you as a programmer
-Everything you should know about coding, programming, machine learning, and data analysis
-How to become an expert in using Python
-Make your program come to life with practical exercises
-How to start profiting from your coding and programming skills
This guide won't only give you theoretical knowledge about Python and coding. It also contains so many small exercises that will help you start your journey of building a program. And, it is this journey that will teach you the most.
Yes, you can learn a lot from reading about Python. But, the best way to learn is by using Python to build something. While you do that, you are quite likely to come across a few obstacles. But don't worry This book will help you conquer each and every one of them.
Ready to become an expert in coding and programming? Grab this book and start your Python journey
Get Your Copy
Автор: Sugomori Yusuke, Kaluza Bostjan, Soares Fбbio M. Название: Deep Learning: Practical Neural Networks with Java ISBN: 1788470311 ISBN-13(EAN): 9781788470315 Издательство: Неизвестно Рейтинг: Цена: 15631.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Suresh Annamalai, Udendhran R., Vimal S. Название: Deep Neural Networks for Multimodal Imaging and Biomedical Applications ISBN: 179983591X ISBN-13(EAN): 9781799835912 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 66614.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presents research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging.
Автор: Graupe Daniel Название: Deep Learning Neural Networks: Design And Case Studies ISBN: 9813146443 ISBN-13(EAN): 9789813146440 Издательство: World Scientific Publishing Цена: 11563.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance.
This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
