Basic Machining Reference Handbook Second Edition Ebook
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Basic Machining Reference Handbook Second Edition Ebook
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Author : Authur R. Meyers
language : en
Publisher: Industrial Press
Release Date : 2004-12-07
Basic Machining Reference Handbook Second Edition Ebook written by Authur R. Meyers and has been published by Industrial Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-12-07 with Technology & Engineering categories.
This Book and CD combines great savings, the book, and the book on CD, fully searchable, with interactive table of contents and index, in Adobe's popular portable document format (PDF). As a comprehensive and easy-to-use hands-on source, Basic Machining Reference Handbook is intended to serve as a memory jog for the experienced, as well as a reference for programmers and others who will not do the machining but do need to know exactly what's involved in performing a given machining step, a series of steps, or a complete job. Remaining true to its original approach, the new second edition continues to present the principles of basic machining, while summarizing the major considerations involved. Logically organized, this time-tested reference starts with those machining steps that most often begin the machining process and moves through the basic machining operations. It is a must-have resource for experienced machinists; programmers; tooling, design and production engineers; and students.
Shop Reference For Students And Apprentices
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Author : Christopher J. McCauley
language : en
Publisher: Industrial Press Inc.
Release Date : 2000
Shop Reference For Students And Apprentices written by Christopher J. McCauley and has been published by Industrial Press Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Science categories.
The perfect handbook for the machine shop, tool room, and drafting room.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Machine Shop Practice
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Author : Karl Hans Moltrecht
language : en
Publisher: Industrial Press Inc.
Release Date : 1981
Machine Shop Practice written by Karl Hans Moltrecht and has been published by Industrial Press Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with Technology & Engineering categories.
Details the skills involved in operating milling cutters, planers, lathes, shaper tools, boring machines, grinding wheels, and drills.
The Metadata Handbook 2nd Ed
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Author : Renée Register
language : en
Publisher: The Future of Publishing
Release Date : 2015-03-14
The Metadata Handbook 2nd Ed written by Renée Register and has been published by The Future of Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-14 with Language Arts & Disciplines categories.
Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10
Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.
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.
Foundations Of Machine Learning Second Edition
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Author : Mehryar Mohri
language : en
Publisher: MIT Press
Release Date : 2018-12-25
Foundations Of Machine Learning Second Edition written by Mehryar Mohri and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-25 with Computers categories.
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Python Data Science Handbook
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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21
Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Tribology Of Abrasive Machining Processes
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Author : Ioan D. Marinescu
language : en
Publisher: William Andrew
Release Date : 2012-12-07
Tribology Of Abrasive Machining Processes written by Ioan D. Marinescu and has been published by William Andrew this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-07 with Technology & Engineering categories.
This book draws upon the science of tribology to understand, predict and improve abrasive machining processes. Pulling together information on how abrasives work, the authors, who are renowned experts in abrasive technology, demonstrate how tribology can be applied as a tool to improve abrasive machining processes. Each of the main elements of the abrasive machining system are looked at, and the tribological factors that control the efficiency and quality of the processes are described. Since grinding is by far the most commonly employed abrasive machining process, it is dealt with in particular detail. Solutions are posed to many of the most commonly experienced industrial problems, such as poor accuracy, poor surface quality, rapid wheel wear, vibrations, work-piece burn and high process costs. This practical approach makes this book an essential tool for practicing engineers. Uses the science of tribology to improve understanding and of abrasive machining processes in order to increase performance, productivity and surface quality of final products A comprehensive reference on how abrasives work, covering kinematics, heat transfer, thermal stresses, molecular dynamics, fluids and the tribology of lubricants Authoritative and ground-breaking in its first edition, the 2nd edition includes 30% new and updated material, including new topics such as CMP (Chemical Mechanical Polishing) and precision machining for micro-and nano-scale applications
Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23
Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.