Machine Learning Brick by Brick, Epoch 1: Using LEGOŽ to Teach Concepts, Algorithms, and Data Structures

Vostokov, Dmitry

ISBN 10: 1912636506 / ISBN 13: 9781912636501

Published by Opentask, 2021

About this Item

Seller Inventory # 44184347-n

Bibliographic Details

• Title: Machine Learning Brick by Brick, Epoch 1: Using LEGOŽ to Teach Concepts, Algorithms, and Data Structures
• Publisher: Opentask
• Publication Date: 2021
• Language: English
• ISBN 10: 1912636506
• ISBN 13: 9781912636501
• Binding: Soft cover
• Condition: New

About this title

Synopsis

This machine learning book series aims at providing real hands-on training from general concepts and architecture to low-level details and mathematics. The first epoch covers the simplest linear associative network, proposes a brick notation for algebraic expressions, shows required calculus derivations, and illustrates gradient descent. Subsequent epochs also include necessary computer science foundations, statistics, data science topics, algorithms, and data structures.

About the Author

Dmitry Vostokov is an internationally recognized expert, speaker, educator, scientist, and author. He is the founder of pattern-oriented software diagnostics, forensics and prognostics discipline, and Software Diagnostics Institute. Vostokov has also authored more than 50 books on software diagnostics, anomaly detection and analysis, software and memory forensics, root cause analysis and problem solving, memory dump analysis, debugging, software trace and log analysis, reverse engineering, and malware analysis. He has more than 25 years of experience in software architecture, design, development, and maintenance in a variety of industries including leadership, technical and people management roles. Dmitry also founded Syndromatix, Anolog.io, BriteTrace, DiaThings, Logtellect, OpenTask Iterative and Incremental Publishing, and Software Diagnostics Technology and Services (former Memory Dump Analysis Services) and Software Prognostics. In his spare time, he presents various topics on Debugging TV and explores Software Narratology, its further development as Narratology of Things and Diagnostics of Things (DoT), Software Pathology, and Quantum Software Diagnostics. His current areas of interest are theoretical software diagnostics and its mathematical and computer science foundations, application of formal logic, artificial intelligence, machine learning and data mining to diagnostics and anomaly detection, software diagnostics engineering and diagnostics-driven development, diagnostics workflow and interaction. Recent interest areas also include cloud native computing, security, automation, functional programming, and applications of category theory to software development and big data.