Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron Название: Deep Learning ISBN: 0262035618 ISBN-13(EAN): 9780262035613 Издательство: Random House (USA) Рейтинг: Цена: 13794.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
If you are curious about the basics of artificial intelligence, blockchain technology, and quantum computing as key enablers for digital transformation and innovation, Digital Fluency is your handy guide. The real-world applications of these cutting-edge technologies are expanding rapidly, and your daily life will continue to be affected by each of them. There is no better time than now to get started and become digitally fluent.
You need not have previous knowledge of these versatile technologies, as author Volker Lang will expertly guide you through this digital age. He illustrates key concepts and applications in numerous practical examples and more than 48 catchy figures throughout Digital Fluency. The end of each chapter presents you with a helpful implementation checklist of central lessons before proceeding to the next. This book gets to the heart of digital buzzwords and concepts, and tells you what they truly mean.
Breaking down topics such as automated driving and intelligent robotics powered by artificial intelligence, blockchain-based cryptocurrencies and smart contracts, drug development and optimization of financial investment portfolios by quantum computing, and more is imperative to being ready for what the future of industry holds. Whether your own digital transformation journey takes place within your private or public organization, your studies, or your individual household, Digital Fluency maps out a concrete digital action plan for all of your technology and innovation strategy needs.
What You Will Learn
Gain guidance in the digital age without requiring any previous knowledge about digital technologies and digital transformation
Get acquainted with the most popular current and prospective applications of artificial intelligence, blockchain technology, and quantum computing across a wide range of industries including healthcare, financial services, and the automobile industry
Become familiar with the digital innovation models of Amazon, Google, Microsoft, IBM, and other world-leading organizations
Implement your own digital transformation successfully along the eight core dimensions of a concrete digital action plan
Who This Book Is For
Thought-leaders, business executives and industry strategists, management and strategy consultants, politicians and policy makers, entrepreneurs, financial analysts, investors and venture capitalists, students and research scientists, as well as general readers, who want to become digitally fluent.
Автор: Warwick Kevin Название: Artificial Intelligence: The Basics ISBN: 0415564832 ISBN-13(EAN): 9780415564830 Издательство: Taylor&Francis Рейтинг: Цена: 3673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Examining the modern origins of artificial intelligence, this book explores issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries.
Автор: Andy Vickler, Vickler Название: Python: python basics for beginners ISBN: 195578633X ISBN-13(EAN): 9781955786331 Издательство: Неизвестно Рейтинг: Цена: 3929.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Are you thinking about learning how to use the Python programming language?
Don`t waste any more time. Scroll up, click on Buy Now, and get started on your Python programming journey!
Mathematical Logic: Mathematics of Logic or Logic Of Mathematics? Zvonimir Sikic
The McCulloch-Pitts Paper from the Perspective of Mathematical Logic Tin Perkov
From the Linguistic Turn to the Cognitive Turn and Back Again Marina Novina
Why not Fuzzy Logic? Ivan Restovic
Meaning as Use: From Wittgenstein to Google's Word2Vec Ines Skelac and Andrej Jandric
Rudolf Carnap, the Grandfather of Artificial Neural Networks: The Influence of Carnap's Philosophy on Walter Pitts Marko Kardum
A Lost Croatian Cybernetic Machine Translation Program Sandro Skansi, Leo Mrsic, and Ines Skelac
The Architecture of Geoffrey Hinton Ivana Stanko
Machine Learning and the Philosophical Problems of Induction Davor Lauc
The Artificial Intelligence Singularity: What it Is and What it Is Not Borna Jalsenjak
AI-Completeness: Using Deep Learning to Eliminate the Human Factor Kristina Sekrst
Transhumanism and Artificial Intelligence: Philosophical Aspects Ivana Greguric Knezevic
Автор: Gupta N., Mangla R. Название: Artificial Intelligence Basics: A Self-Teaching Introduction ISBN: 1683925165 ISBN-13(EAN): 9781683925163 Издательство: Неизвестно Рейтинг: Цена: 6620.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Designed as a self-teaching introduction to the fundamental concepts of artificial intelligence, this book begins with its history, the Turing test, and early applications. Later chapters cover the basics of searching, game playing, and knowledge representation. Expert systems and machine learning are also covered in detail.
Описание: Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks.
You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow.
It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn:* Explains basics to advanced concepts of time series* How to design, develop, train, and validate time-series methodologies* What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results* Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series.
* Univariate and multivariate problem solving using fbprophet. Who This Book Is ForData scientists, data analysts, financial analysts, and stock market researchers
Автор: Esra Bas Название: Basics of Probability and Stochastic Processes ISBN: 3030323226 ISBN-13(EAN): 9783030323226 Издательство: Springer Рейтинг: Цена: 6707.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook explores probability and stochastic processes at a level that does not require any prior knowledge except basic calculus. It presents the fundamental concepts in a step-by-step manner, and offers remarks and warnings for deeper insights. The chapters include basic examples, which are revisited as the new concepts are introduced. To aid learning, figures and diagrams are used to help readers grasp the concepts, and the solutions to the exercises and problems. Further, a table format is also used where relevant for better comparison of the ideas and formulae. The first part of the book introduces readers to the essentials of probability, including combinatorial analysis, conditional probability, and discrete and continuous random variable. The second part then covers fundamental stochastic processes, including point, counting, renewal and regenerative processes, the Poisson process, Markov chains, queuing models and reliability theory. Primarily intended for undergraduate engineering students, it is also useful for graduate-level students wanting to refresh their knowledge of the basics of probability and stochastic processes.
