Tag: Machine

The Soul of a New Machine


Free Download The Soul of a New Machine by Tracy Kidder, Ben Sullivan, Hachette Audio
English | 2016 | ISBN: B01FCTJCR0 | MP3@64 kbps | 9 hrs 3 mins | 249 Mb
Computers have changed since 1981, when Tracy Kidder memorably recorded the drama, comedy, and excitement of one company’s efforts to bring a new microcomputer to market. What has not changed is the feverish pace of the high-tech industry, the go-for-broke approach to business that has caused so many computer companies to win big (or go belly up), and the cult of pursuing mind-bending technological innovations. The Soul of a New Machine is an essential chapter in the history of the machine that revolutionized the world in the 20th century.

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Signal Processing and Machine Learning Theory


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by Diniz, Paulo S. R.;

English | 2023 | ISBN: 0323917720 | 1236 pages | True PDF | 41.42 MB
Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc.

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Serverless Machine Learning with Amazon Redshift ML Create, train and deploy machine learning models using familiar SQL


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by Debu Panda, Phil Bates, Bhanu Pittampally, and Sumeet Joshi

English | 2023 | ISBN: 1804619280 | 290 pages | True/Retail PDF EPUB | 29.86 MB
Supercharge and deploy Amazon Redshift Serverless, train and deploy Machine learning Models using Amazon Redshift ML and run inference queries at scale.

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Programming the Finite State Machine with 8-Bit PICs in Assembly and C


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by Pratt, Andrew;

English | 2020 | ISBN: 1907920927 | 188 pages | True PDF | 10.85 MB
This practical guide is aimed at electronics students and hobbyists. It is intended to be a valuable aid in writing programs using Finite State Machines (FSMs) in assembly language using 8-bit PIC microcontrollers. The last two chapters introduce the use of the C programming language and make a direct comparison with development in Assembly.

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Machine Learning in Pure Mathematics and Theoretical Physics


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by Yang-Hui He

English | 2023 | ISBN: 1800613695 | 418 pages | True PDF | 32.41 MB
The juxtaposition of "machine learning" and "pure mathematics and theoretical physics" may first appear as contradictory in terms. The rigours of proofs and derivations in the latter seem to reside in a different world from the randomness of data and statistics in the former. Yet, an often under-appreciated component of mathematical discovery, typically not presented in a final draft, is experimentation: both with ideas and with mathematical data. Think of the teenage Gauss, who conjectured the Prime Number Theorem by Descriptionting the prime-counting function, many decades before complex analysis was formalized to offer a proof. Can modern technology in part mimic Gauss’s intuition? The past five years saw an explosion of activity in using AI to assist the human mind in uncovering new mathematics: finding patterns, accelerating computations, and raising conjectures via the machine learning of pure, noiseless data. The aim of this book, a first of its kind, is to collect research and survey articles from experts in this emerging dialogue between theoretical mathematics and machine learning. It does not dwell on the well-known multitude of mathematical techniques in deep learning, but focuses on the reverse relationship: how machine learning helps with mathematics. Taking a panoramic approach, the topics range from combinatorics to number theory, and from geometry to quantum field theory and string theory. Aimed at PhD students as well as seasoned researchers, each self-contained chapter offers a glimpse of an exciting future of this symbiosis.

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Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R


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by Aydede, Yigit;

English | 2024 | ISBN: 1032463953 | 601 pages | True PDF EPUB | 38.87 MB
Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields.

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Linear Algebra And Optimization With Applications To Machine Learning – Volume I Linear Algebra For Computer Vision, Robotics


Free Download Linear Algebra And Optimization with Applications to Machine Learning Volume I: Linear Algebra for Computer Vision, Robotics, and Machine Learning (822 Pages)
by Jean Gallier & Jocelyn Quaintance

English | 2021 | ISBN: 9811206392 | 823 pages | True/Retail PDF | 7.11 MB
This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.

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Intelligent Prognostics for Engineering Systems with Machine Learning Techniques


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by Edited by Gunjan Soni

English | 2023 | ISBN: 1032054360 | 261 pages | True PDF | 67.54 MB
The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science. The book The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science.

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