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 08/16/2022
1702-002303	006.3 Bud-Fun 2017	General Collection	SEEU Library Skopje	English	Not for loan

The hundred-page machine learning book / Andriy Burkov

No review available on this record. Title : The hundred-page machine learning book Material Type: printed text Authors: Andriy Burkov, Author Publisher: (Author) Publication Date: 2019 Pagination: xviii, 141 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-999579-50-0 General note: Includes index Languages : English (eng) Original Language : English (eng) Descriptors: Machine learning Materials science - Data processing Class number: 006.31 Abstract: Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. Contents note: Notation and definitions; Fundamental algorithms; Anatomy of a learning algorithm; Basic practice; Neural networks and deep learning; Problems and solutions; Advanced practice; Unsupervised learning; Other forms of learning; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=21500

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Barcode	Call number	Media type	Location	Section	Status
1702-002653	006.31 Bur-Hun 2019	General Collection	SEEU Library Skopje	English	Due for return by 02/15/2023

Implementing useful algorithms in C++ / Dmytro Kedyk

No review available on this record. Title : Implementing useful algorithms in C++ Material Type: printed text Authors: Dmytro Kedyk, Author Publisher: (Author) Publication Date: 2020 Pagination: 684 p. Layout: ill. Size: 30 cm ISBN (or other code): 979-86-05-32530-7 General note: Includes index (p. 675-684) Includes bibliographical references Languages : English (eng) Original Language : English (eng) Descriptors: C++ (Computer program language) Computer algorithms Machine learning Class number: 005.13 Abstract: Programmers use algorithms and data structures all the time, usually through numerous available APIs. Ideally an algorithm is correct, easy to understand, applicable to many problems, efficient, and free of intellectual property claims. This book covers algorithms and data structures learned in an algorithms class and many that aren't, including statistical algorithms, external memory algorithms, numerical methods, optimization, string algorithms, and data compression. About a fourth of the book is devoted to machine learning. There is much more theory than in the rest of the book because in machine learning relevant theory is more practical than algorithms. New learning algorithms are proposed often, and it's easy to get lost without understanding how learning actually works. In particular, getting comfortable with the concept of estimation error substantially improves the ability to reason about statistical algorithms. Another fourth is about numerical algorithms. Separate chapters cover matrix algorithms (such as eigenvalue calculation for spectral clustering), working with functions (integration, root finding, etc.), and optimization (both continuous and convex). Expect to learn something new in every chapter. Many topics appear only in specialized books and papers, including collections of random number generators and hash functions for various use cases, priority queues that allow random access for applications like Djikstra's shortest path algorithm, the simplex method for linear programming, efficient dictionaries for variable-length keys, Monte Carlo and bootstrap methods for statistical computing, top-performing learning algorithms such as random forest, etc. One of the goals of the book is answering all questions you might have had since taking an algorithms class. Algorithm descriptions have tested C++ code, illustrations, performance analysis, and suggestions for optimizations and extensions. Still, the book is advanced, requiring some algorithmic maturity. After working through it, you will know more about algorithms and machine learning than before, even if you are already an expert. This is the book the author wishes he had when he started studying algorithms. Contents note: Software engineering essentials; Career advice and interviews; Introduction to computer law; Fundamental data structures; Random number generation; Sorting; Dynamic sorted sequences; Hashing; Priority queues; Graph algorithms; Miscellaneous algorithms and techniques; External memory algorithms; String algorithms; Compression; Combinatorial optimization; Large numbers; Computational geometry; Error detection and correction; Cryptography; Computational statistics; Numerical algorithms; Introduction and matrix algebra; Numerical algorithms--working with functions; Numerical optimization; General machine learning; Machine learning; Classification; Machine learning; regression; Machine learning- clustering; Machine learning- other tasks; Scrap-not useful algorithms and data structures; Appendix -C++ notes; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=21258

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Barcode	Call number	Media type	Location	Section	Status
1702-002558	005.13 Ked-Imp 2020	General Collection	Library "Max van der Stoel"	English	Due for return by 11/24/2022
1702-002557	005.13 Ked-Imp 2020	General Collection	SEEU Library Skopje	English	Due for return by 12/21/2022

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