Author: Publisher:
ISBN:
Category : American literature
Languages : en
Pages : 2262
Book Description
A world list of books in the English language.
Author: Paul Fussell
Author: Authentic Diversity
Author: Michelle SilverthornPublisher: CRC Press
ISBN: 042966303X
Category : Business & Economics
Languages : en
Pages : 140
Book Description
The nation has transformed. The calls for racial equity are loud and insistent and they are now being listened to. And yet, companies across the country are still far behind when it comes to equity in the workplace. For decades, we've heard variations on the same theme on how to increase diversity and inclusion and we have still not moved. If we want equity to matter inside and outside the workplace, if we want to be real allies for change, then we need a new approach. We need to stop following trends. We need to lead change. In Authentic Diversity, culture change expert and diversity speaker, Michelle Silverthorn, explains how to transform diversity and inclusion from mere lip service into the very heart of leadership. Following the journey of a Black woman in the workplace, leaders learn the old rules of diversity that keep failing her and millions like her again and again, and the new rules they must put in place to make success a reality for everyone. A millennial, immigrant, and Black woman in America, Michelle will show you how to lead a space centered on equity, allyship, and inclusion and how together we can build a new organization, and nation, centered on justice.
Deep Learning
Author: Manel Martinez-RamonPublisher: John Wiley & Sons
ISBN: 1119861861
Category : Computers
Languages : en
Pages : 421
Book Description
An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning tools Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures Practical discussions of recurrent neural networks and non-supervised approaches to deep learning Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.
The Book of R
Author: Tilman M. DaviesPublisher: No Starch Press
ISBN: 1593276516
Category : Computers
Languages : en
Pages : 833
Book Description
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.
Mathematics for Machine Learning
Author: Marc Peter DeisenrothPublisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Introduction to Autonomous Robots
Author: Nikolaus CorrellPublisher:
ISBN: 9780692700877
Category :
Languages : en
Pages : 226
Book Description
This book introduces concepts in mobile, autonomous robotics to 3rd-4th year students in Computer Science or a related discipline. The book covers principles of robot motion, forward and inverse kinematics of robotic arms and simple wheeled platforms, perception, error propagation, localization and simultaneous localization and mapping. The cover picture shows a wind-up toy that is smart enough to not fall off a table just using intelligent mechanism design and illustrate the importance of the mechanism in designing intelligent, autonomous systems. This book is open source, open to contributions, and released under a creative common license.
Author: Tor LattimoreIntelligent Systems and Sustainable Computing
Author: V. Sivakumar ReddyPublisher: Springer Nature
ISBN: 9811900116
Category : Technology & Engineering
Languages : en
Pages : 662
Book Description
The book is a collection of best selected research papers presented at the International Conference on Intelligent Systems and Sustainable Computing (ICISSC 2021), held in School of Engineering, Malla Reddy University, Hyderabad, India, during 24–25 September 2021. The book covers recent research in intelligent systems, intelligent business systems, soft computing, swarm intelligence, artificial intelligence and neural networks, data mining & data warehousing, cloud computing, distributed computing, big data analytics, Internet of Things (IoT), machine learning, speech processing, sustainable high-performance systems, VLSI and embedded systems, image and video processing, and signal processing and communication.
