Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 1493938436 ISBN-13(EAN): 9781493938438 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Автор: Zhang Wengang, Zhang Yanmei, Gu Xin Название: Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience ISBN: 9811668345 ISBN-13(EAN): 9789811668340 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book summarizes the application of soft computing techniques, machine learning approaches, deep learning algorithms and optimization techniques in geoengineering including tunnelling, excavation, pipelines, etc.
Автор: Suganthi K., Karthik R., Rajesh G. Название: Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems ISBN: 0367620065 ISBN-13(EAN): 9780367620066 Издательство: Taylor&Francis Рейтинг: Цена: 117390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book offers the latest advances and results in the fields of machine learning and deep learning for wireless communications and provides positive discussions on the challenges and prospects. It includes a broad spectrum in understanding the improvements motivated by specific constraints posed by wireless communications.
Автор: Kulkarni, Akshay Shivananda, Adarsha Название: Natural language processing recipes ISBN: 1484242661 ISBN-13(EAN): 9781484242667 Издательство: Springer Рейтинг: Цена: 30740.00 T Наличие на складе: Невозможна поставка. Описание:
Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity extraction, and sentiment analysis.
Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You’ll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing.
By using the recipes in this book, you will have a toolbox of solutions to apply to your own projects in the real world, making your development time quicker and more efficient.
What You Will Learn
Apply NLP techniques using Python libraries such as NLTK, TextBlob, spaCy, Stanford CoreNLP, and many moreImplement the concepts of information retrieval, text summarization, sentiment analysis, and other advanced natural language processing techniques.Identify machine learning and deep learning techniques for natural language processing and natural language generation problems
Who This Book Is For
Data scientists who want to refresh and learn various concepts of natural language processing through coding exercises.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 239310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Learn Machine Learning, Deep Learning, Data Science and More
Machine learning is here; it is changing the world in ways you might not know yet. From search engines to speech recognition on your phone, machine learning is taking over.
If you have taken an interest in machine learning and want to learn how it all works, then you need some guidance before you can dive-in to the complicated stuff.
This book explains machine learning, in simple English, for beginners of all levels.
In this book, you will learn how machines are able to use data to learn on their own, discover how you can create sophisticated programs without the need for complex programming, and see daily applications of machine learning in action
Here's what you will find inside:
Introduction to machine learning from history, types of machine learning and examples.
Basics of machine learning: You will learn about datasets and see examples of the ones you can download
Machine learning algorithms: You will learn about neural networks and see practical applications of machine learning and deep learning algorithms
Machine learning software: You will get started with machine learning and see some of the most popular scientific computing software platforms.
Artificial intelligence and why it is important: You will learn how artificial intelligence relates to machine learning and what the future looks like.
You will get access to datasets and machine learning software so you can try out your very own machine learning project.
FAQ
Q: Do I need prior programming experience to make use of the book?
A: No. This book is intended for complete beginners to machine learning. The language used is simple and the reader is taken from one concept to the next in a progressive manner.
Q: Will this book make an expert in machine learning?
A: This book is intended to give beginners a firm introduction into machine learning so they are better placed to understand advanced machine learning concepts.
Автор: Kamath, Uday, Название: Transformers for machine learning : ISBN: 0367767341 ISBN-13(EAN): 9780367767341 Издательство: Taylor&Francis Рейтинг: Цена: 45930.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. This is the first comprehensive book on transformers.
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: Kulkarni Название: Time Series Algorithms Recipes ISBN: 1484289773 ISBN-13(EAN): 9781484289778 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn * Implement various techniques in time series analysis using Python. * Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting * Understand univariate and multivariate modeling for time series forecasting * Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is For Data Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
Автор: Hitoshi Iba Название: Evolutionary Approach to Machine Learning and Deep Neural Networks ISBN: 9811343586 ISBN-13(EAN): 9789811343582 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gr?bner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Название: Deep learning in biometrics ISBN: 1138578231 ISBN-13(EAN): 9781138578234 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book will cover all the topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoenders. The focus will be on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints.
Автор: Lanham Micheal Название: Learn Unity ML - Agents - Fundamentals of Unity Machine Learning ISBN: 1789138132 ISBN-13(EAN): 9781789138139 Издательство: Неизвестно Рейтинг: Цена: 40450.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Unity Machine Learning Agents allows researchers and developers to create games and simulations using the Unity Editor which serve as environments where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep ...
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