Building intelligent systems / Geoff Hulten

No review available on this record. Title : Building intelligent systems : a guide to machine learning in practice Material Type: printed text Authors: Geoff Hulten, Author Publisher: New York : Apress Publication Date: 2018 Pagination: xxvi, 339 p. Size: 26 cm ISBN (or other code): 978-1-484-23431-0 General note: Includes index (p. 331-339) Languages : English (eng) Original Language : English (eng) Descriptors: Artificial intelligence Computer science Data processing Machine learning Class number: 006.31 Abstract: "Building intelligent systems"- Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. What You'll Learn Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success Design an intelligent user experience: Produce data to help make the Intelligent System better over time Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice Create intelligence: Use different approaches, including machine learning Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want Who This Book Is For Software engineers, machine learning practitioners, and technical managers who want to build effective intelligent systems Contents note: Introduction; Approaching an intelligent systems project; Intelligent experiences; Implementing intelligence; Creating intelligence; Orchestrating intelligence systems; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=18075

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Barcode	Call number	Media type	Location	Section	Status
1702-002334	006.31 Hul-Bui 2018	General Collection	Library "Max van der Stoel"	English	Available
1702-002327	006.31 Hul-Bui 2018	General Collection	SEEU Library Skopje	English	Available

Deep learning / Ian Goodfellow

No review available on this record. Title : Deep learning Material Type: printed text Authors: Ian Goodfellow, Author ; Yoshua Bengio, Author ; Aaron Courville, Author Publisher: Cambridge, Massachusets. : MIT Press Publication Date: 2016 Pagination: xxii- 775 p Layout: ill Size: 24 cm ISBN (or other code): 978-0-262-03561-3 General note: Includes bibliographical references (pages 711-766) and index. Languages : English (eng) Original Language : English (eng) Descriptors: Machine learning Class number: 006.3 Abstract: "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. Contents note: Applied math and machine learning basics. Linear algebra; Probability and information theory; Numerical computation; Machine learning basics; Deep networks: modern practices. Deep feedforward networks; Regularization for deep learning; Optimization for training deep models; Convolutional networks; Sequence modeling: recurrent and recursive nets; Practical methodology; Applications; Deep learning research. Linear factor models; Autoencoders; Representation learning; Structured probabilistic models for deep learning; Monte Carlo methods; Confronting the partition function; Approximate inference; Deep generative models. Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=18436

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Barcode	Call number	Media type	Location	Section	Status
1702-002313	006.3 Goo-Dee 2016	General Collection	Library "Max van der Stoel"	English	Available

Fundamentals of deep learning / Nikhil Buduma

No review available on this record. Title : Fundamentals of deep learning : designing next-generation machine intelligence algorithms Material Type: printed text Authors: Nikhil Buduma, Author Publisher: O'Reilly Media, Inc, USA Publication Date: 2017 Pagination: xii-283 p Layout: ill Size: 24 cm ISBN (or other code): 978-1-491-92561-4 General note: Includes bibliographical references and index. Languages : English (eng) Original Language : English (eng) Descriptors: Artificial intelligence Deep learning Machine learning Neural networks (Computer science) Class number: 006.3 Abstract: "Fundamentals of deep learning : designing next-generation machine intelligence algorithms" - With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Understand the fundamentals of reinforcement learning Contents note: The neural network; Training feed-forward neural networks; Implementing neural networks in TensorFlow; Beyond gradient descent; Convolutional neural networks; Embedding and representation learning; Models for sequence analysis; Memory augmented neural networks; Deep reinforcement learning. Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=18469

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Barcode	Call number	Media type	Location	Section	Status
1702-002304	006.3 Bud-Fun 2017	General Collection	Library "Max van der Stoel"	English	Due for return by 12/15/2021
1702-002303	006.3 Bud-Fun 2017	General Collection	SEEU Library Skopje	English	Not for loan

Pattern recognition and machine learning / Christopher M. Bishop

No review available on this record. Title : Pattern recognition and machine learning Material Type: printed text Authors: Christopher M. Bishop, Author Publisher: New York : Springer Publication Date: 2006 Pagination: xx; 738 p. Size: 25 cm ISBN (or other code): 978-0-387-31073-2 General note: Includes appendix (p. 677-710) Includes bibliographical references (p. 711-728) Includes index (p. 729-738) Languages : English (eng) Original Language : English (eng) Descriptors: Machine learning Pattern recognition systems - Data processing Class number: 006.4 Abstract: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. 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. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Contents note: Introduction; Probability distributions; Linear models of regression; Linear models for classification; Neural networks; Kernel methods; Sparse kernel machines; Graphical models; Mixture models and EM; Approximate inference; Sampling methods; Continuous latent variables; Sequential data; Combining models; Data sets; Probability distributions; Properties of matrices; Calculus of variations; Lagrange multipliers; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=16042

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Barcode	Call number	Media type	Location	Section	Status
1702-001581	006.4 Bis-Pat 2006	General Collection	Library "Max van der Stoel"	English	Not for loan

Python machine learning cookbook / Prateek Joshi

No review available on this record. Title : Python machine learning cookbook : 100 recipes that teach you how to perform various machine learning tasks in the real world Material Type: printed text Authors: Prateek Joshi, Author Publisher: Birmingham : Packt Publishing Publication Date: 2016 Pagination: x, 286 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-7864-6447-7 General note: Includes index Languages : English (eng) Original Language : English (eng) Descriptors: Machine learning Python (Computer program language) Class number: 006.31 Abstract: With Python Machine Learning Cookbook, you will learn how to perform carious machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithm to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithm, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-worlds examples. Contents note: The realm of supervised learning; Constructing a classifier; Predictive modeling; Clustering with unsupervised learning; Building recommendation engines; Analyzing text data; Speech recognition; Dissecting time series and sequential data; Image content analysis; Biometric face recognition; Deep neural networks; Visualizing data; Index; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17508

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Barcode	Call number	Media type	Location	Section	Status
1702-002415	006.31 Jos-Pyt 2016	General Collection	SEEU Library Skopje	English	Not for loan

Python machine learning / Sebastian Raschka

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