Fundamentals and methods of machine and deep learning, Singh, Pardeep
Автор: Saleh Hyatt Название: Machine Learning Fundamentals ISBN: 1789803551 ISBN-13(EAN): 9781789803556 Издательство: Неизвестно Рейтинг: Цена: 7171.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains the scikit-learn API, which is a package created to facilitate the process of building machine learning applications. By explaining the differences between supervised and unsupervised models and by ...
Автор: Mathar Rudolf, Alirezaei Gholamreza, Balda Emilio Название: Fundamentals of Data Analytics: With a View to Machine Learning ISBN: 3030568334 ISBN-13(EAN): 9783030568337 Издательство: Springer Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces the basic methodologies for successful data analytics. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.
If you are looking for a complete beginners guide to learn deep learning with examples, in just a few hours, then you need to continue reading.
This book delves into the basics of deep learning for those who are enthusiasts concerning all things machine learning and artificial intelligence. For those who have seen movies that show computer systems taking over the world like, Terminator, or benevolent systems that watch over the population, i.e. Person of Interest, this should be right up your alley.
This book will give you the basics of what deep learning entails. That means frameworks used by coders and significant components and tools used in deep learning, that enable facial recognition, speech recognition, and virtual assistance. Yes, deep learning provides the tools through which systems like Siri became possible.
Grab your copy today and learn:
Deep learning utilizes frameworks that allow people to develop tools that are able to offer better abstraction, along with simplification of hard programming issues. TensorFlow is the most popular tool and is used by corporate giants such as Airbus, Twitter, and even Google.
The book illustrates TensorFlow and Caffe2 as the prime frameworks that are used for development by Google and Facebook. Facebook illustrates Caffe2 as one of the lightweight and modular deep learning frameworks, though TensorFlow is the most popular one, considering it has a lot of popularity, and thus, a big forum, which allows for assistance on main problems.
The book considers several components and tools of deep learning such as the neural networks; CNNs, RNNs, GANs, and auto-encoders. These algorithms create the building blocks which propel deep learning and advance it.
The book also considers several applications, including chatbots and virtual assistants, which have become the main focus for deep learning into the future, as they represent the next frontier in information gathering and connectivity. The Internet of Things is also represented here, as deep learning allows for integration of various systems via an artificial intelligence system, which is already being used for the home and car functions.
And much more...
The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow.
This book is probably one of the best books for beginners. It's a step-by-step guide for any person who wants to start learning deep learning and artificial intelligence from scratch.
When data science can reduce spending costs by billions of dollars in our economy, why wait to jump in?
Описание: Systems theory and stability concepts.- Main approaches to nonlinear control.- Main approaches to nonlinear estimation.- Linearizing control and filtering for nonlinear dynamics in financial systems.- Nonlinear optimal control and filtering for financial systems.- Kalman Filtering Approach for detection of option mispricing inthe Black-Scholes PDE.- Kalman Filtering approach to the detection of option mispricing inelaborated PDE finance models.- Corporations' default probability forecasting using theDerivative-free nonlinear Kalman Filter.- Validation of financial options models using neural networks with invariance to Fourier transform.- Statistical validation of financial forecasting tools with generalized likelihood ratio approaches.- Distributed validation of option price forecasting tools using a statistical fault diagnosis approach.- Stabilization of financial systems dynamics through feedbackcontrol of the Black-Scholes PDE.- Stabilization of the multi-asset Black-Scholes PDE using differentialflatness theory.- Stabilization of commodities pricing PDE using differential flatnesstheory.- Stabilization of mortgage price dynamics using differential flatness theory.v>
Описание: Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG).
This is because the aims of these fields are to build systems that understand what people mean when they speak or write, and that can produce linguistic strings that successfully express to people the intended content. In order for NLP to scale beyond partial, task-specific solutions, researchers in these fields must be informed by what is known about how humans use language to express and understand communicative intents. The purpose of this book is to present a selection of useful information about semantics and pragmatics, as understood in linguistics, in a way that's accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics.
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 ...
Автор: Mathar Rudolf, Alirezaei Gholamreza, Balda Emilio Название: Fundamentals of Data Analytics: With a View to Machine Learning ISBN: 303056830X ISBN-13(EAN): 9783030568306 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces the basic methodologies for successful data analytics. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.
Автор: Kaizhu Huang; Amir Hussain; Qiu-Feng Wang; Rui Zha Название: Deep Learning: Fundamentals, Theory and Applications ISBN: 3030060721 ISBN-13(EAN): 9783030060725 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
Описание: The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes.Behaviour analysis and anomaly detection are key components of intelligent vision systems.
Автор: Alan H. Fielding Название: Machine Learning Methods for Ecological Applications ISBN: 1461374138 ISBN-13(EAN): 9781461374138 Издательство: Springer Рейтинг: Цена: 22203.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem.
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.
Автор: Trappenberg Thomas Название: Fundamentals of Machine Learning ISBN: 0198828047 ISBN-13(EAN): 9780198828044 Издательство: Oxford Academ Рейтинг: Цена: 6572.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Interest in machine learning is exploding across the world, both in research and for industrial applications. Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to both students and researchers.
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