Download or read book Ultralearning written by Scott Young and published by HarperCollins. This book was released on 2019-08-06 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now a Wall Street Journal bestseller. Learn a new talent, stay relevant, reinvent yourself, and adapt to whatever the workplace throws your way. Ultralearning offers nine principles to master hard skills quickly. This is the essential guide to future-proof your career and maximize your competitive advantage through self-education. In these tumultuous times of economic and technological change, staying ahead depends on continual self-education—a lifelong mastery of fresh ideas, subjects, and skills. If you want to accomplish more and stand apart from everyone else, you need to become an ultralearner. The challenge of learning new skills is that you think you already know how best to learn, as you did as a student, so you rerun old routines and old ways of solving problems. To counter that, Ultralearning offers powerful strategies to break you out of those mental ruts and introduces new training methods to help you push through to higher levels of retention. Scott H. Young incorporates the latest research about the most effective learning methods and the stories of other ultralearners like himself—among them Benjamin Franklin, chess grandmaster Judit Polgár, and Nobel laureate physicist Richard Feynman, as well as a host of others, such as little-known modern polymath Nigel Richards, who won the French World Scrabble Championship—without knowing French. Young documents the methods he and others have used to acquire knowledge and shows that, far from being an obscure skill limited to aggressive autodidacts, ultralearning is a powerful tool anyone can use to improve their career, studies, and life. Ultralearning explores this fascinating subculture, shares a proven framework for a successful ultralearning project, and offers insights into how you can organize and exe - cute a plan to learn anything deeply and quickly, without teachers or budget-busting tuition costs. Whether the goal is to be fluent in a language (or ten languages), earn the equivalent of a college degree in a fraction of the time, or master multiple tools to build a product or business from the ground up, the principles in Ultralearning will guide you to success.

Download or read book Mastering Linear Algebra written by Francis Su and published by . This book was released on 2019-03-20 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mastering Linear Algebra written by Cybellium Ltd and published by Cybellium Ltd. This book was released on with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the Language of Vectors and Matrices for Enhanced Problem Solving In the realm of mathematics and science, linear algebra stands as a powerful language that underlies numerous disciplines. "Mastering Linear Algebra" is your definitive guide to understanding and harnessing the potential of this essential mathematical framework, empowering you to solve complex problems with clarity and precision. About the Book: As mathematical concepts become more integral to various fields, a strong grasp of linear algebra becomes increasingly valuable. "Mastering Linear Algebra" offers a comprehensive exploration of this foundational subject—a cornerstone of mathematics and its applications. This book caters to both newcomers and experienced learners aiming to excel in linear algebra concepts, computations, and applications. Key Features: Linear Algebra Fundamentals: Begin by understanding the core principles of linear algebra. Learn about vectors, matrices, and linear transformations—the fundamental building blocks of the subject. Matrix Operations: Dive into matrix operations. Explore techniques for matrix addition, multiplication, inversion, and determinant computation. Vector Spaces: Grasp the art of vector spaces and subspaces. Understand how to define, visualize, and analyze vector spaces for various applications. Eigenvalues and Eigenvectors: Explore the significance of eigenvalues and eigenvectors. Learn how they enable the analysis of dynamic systems and transformations. Linear Systems: Understand how linear algebra solves systems of linear equations. Explore techniques for Gaussian elimination, LU decomposition, and matrix factorization. Applications in Science and Engineering: Delve into real-world applications of linear algebra. Discover how it's applied in physics, computer graphics, data analysis, and more. Inner Product Spaces: Grasp the concepts of inner product spaces and orthogonality. Explore applications in geometric interpretations and least-squares solutions. Singular Value Decomposition: Explore the power of singular value decomposition. Understand how it enables data compression, noise reduction, and dimensionality reduction. Why This Book Matters: In a world driven by data and technological advancement, mastering linear algebra offers a competitive edge. "Mastering Linear Algebra" empowers students, researchers, scientists, and technology enthusiasts to leverage this fundamental mathematical language, enabling them to analyze and solve problems across diverse fields. Unlock the Power of Mathematical Insight: In the landscape of mathematics and science, linear algebra is the key to understanding complex relationships and transformations. "Mastering Linear Algebra" equips you with the knowledge needed to leverage linear algebra concepts, enabling you to solve intricate problems with clarity and precision. Whether you're a seasoned learner or new to the world of linear algebra, this book will guide you in building a solid foundation for effective mathematical analysis and application. Your journey to mastering linear algebra starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Download or read book Basics of Linear Algebra for Machine Learning written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-01-24 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this laser-focused Ebook, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.

Download or read book Topology Through Inquiry written by Michael Starbird and published by American Mathematical Soc.. This book was released on 2020-09-10 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Topology Through Inquiry is a comprehensive introduction to point-set, algebraic, and geometric topology, designed to support inquiry-based learning (IBL) courses for upper-division undergraduate or beginning graduate students. The book presents an enormous amount of topology, allowing an instructor to choose which topics to treat. The point-set material contains many interesting topics well beyond the basic core, including continua and metrizability. Geometric and algebraic topology topics include the classification of 2-manifolds, the fundamental group, covering spaces, and homology (simplicial and singular). A unique feature of the introduction to homology is to convey a clear geometric motivation by starting with mod 2 coefficients. The authors are acknowledged masters of IBL-style teaching. This book gives students joy-filled, manageable challenges that incrementally develop their knowledge and skills. The exposition includes insightful framing of fruitful points of view as well as advice on effective thinking and learning. The text presumes only a modest level of mathematical maturity to begin, but students who work their way through this text will grow from mathematics students into mathematicians. Michael Starbird is a University of Texas Distinguished Teaching Professor of Mathematics. Among his works are two other co-authored books in the Mathematical Association of America's (MAA) Textbook series. Francis Su is the Benediktsson-Karwa Professor of Mathematics at Harvey Mudd College and a past president of the MAA. Both authors are award-winning teachers, including each having received the MAA's Haimo Award for distinguished teaching. Starbird and Su are, jointly and individually, on lifelong missions to make learning—of mathematics and beyond—joyful, effective, and available to everyone. This book invites topology students and teachers to join in the adventure.

Download or read book Mastering Differential Equations written by Teaching Company and published by . This book was released on 2011 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this course, Boston University Professor Robert L. Devaney presents an introduction to differential equations.

Download or read book Linear Algebra and Its Applications written by David C. Lay and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With traditional linear algebra texts, the course is relatively easy for students during the early stages as material is presented in a familiar, concrete setting. However, when abstract concepts are introduced, students often hit a wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations) are not easily understood and require time to assimilate. These concepts are fundamental to the study of linear algebra, so students' understanding of them is vital to mastering the subject. This text makes these concepts more accessible by introducing them early in a familiar, concrete Rn setting, developing them gradually, and returning to them throughout the text so that when they are discussed in the abstract, students are readily able to understand. KEY TOPICS: Linear Equations in Linear Algebra; Matrix Algebra; Determinants; Vector Spaces; Eigenvalues and Eigenvectors; Orthogonality and Least Squares; Symmetric Matrices and Quadratic Forms; The Geometry of Vector Spaces; Optimization; Finite-State Markov Chains MARKET: for all readers interested in linear algebra. NOTE: Before purchasing, check with your instructor to ensure you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for each title, and registrations are not transferable. To register for and use Pearson's MyLab & Mastering products, you may also need a Course ID, which your instructor will provide. Used books, rentals, and purchases made outside of Pearson If purchasing or renting from companies other than Pearson, the access codes for Pearson's MyLab & Mastering products may not be included, may be incorrect, or may be previously redeemed. Check with the seller before completing your purchase. Note: You are purchasing a standalone product; MyMathLab does not come packaged with this content. MyMathLab is not a self-paced technology and should only be purchased when required by an instructor. If you would like to purchase both the physical text and MyMathLab, search for: 9780134022697 / 0134022696 Linear Algebra and Its Applications plus New MyMathLab with Pearson eText -- Access Card Package, 5/e

Download or read book A First Course in Linear Algebra written by Minking Eie and published by World Scientific Publishing Company. This book was released on 2016 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The text itself is well-written. Each chapter begins with a brief synopsis which nicely captures the heart of the chapter, and there are enough boldface headings and vocabulary to keep the reader oriented to the task at hand." MAA Reviews A First Course in Linear Algebra is written by two experts from algebra who have more than 20 years of experience in algebra, linear algebra and number theory. It prepares students with no background in Linear Algebra. Students, after mastering the materials in this textbook, can already understand any Linear Algebra used in more advanced books and research papers in Mathematics or in other scientific disciplines. This book provides a solid foundation for the theory dealing with finite dimensional vector spaces. It explains in details the relation between linear transformations and matrices. One may thus use different viewpoints to manipulate a matrix instead of a one-sided approach. Although most of the examples are for real and complex matrices, a vector space over a general field is briefly discussed. Several optional sections are devoted to applications to demonstrate the power of Linear Algebra.

Download or read book A First Course In Linear Algebra written by Shou-te Chang and published by World Scientific Publishing Company. This book was released on 2016-07-13 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: A First Course in Linear Algebra is written by two experts from algebra who have more than 20 years of experience in algebra, linear algebra and number theory. It prepares students with no background in Linear Algebra. Students, after mastering the materials in this textbook, can already understand any Linear Algebra used in more advanced books and research papers in Mathematics or in other scientific disciplines.This book provides a solid foundation for the theory dealing with finite dimensional vector spaces. It explains in details the relation between linear transformations and matrices. One may thus use different viewpoints to manipulate a matrix instead of a one-sided approach. Although most of the examples are for real and complex matrices, a vector space over a general field is briefly discussed. Several optional sections are devoted to applications to demonstrate the power of Linear Algebra.

Download or read book Linear Algebra written by Hugo J. Woerdeman and published by CRC Press. This book was released on 2021-03-04 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is good reason to be excited about Linear Algebra. With the world becoming increasingly digital, Linear Algebra is gaining more and more importance. When we send texts, share video, do internet searches, there are Linear Algebra algorithms in the background that make it work. This concise introduction to Linear Algebra is authored by a leading researcher presents a book that covers all the requisite material for a first course on the topic in a more practical way. The book focuses on the development of the mathematical theory and presents many applications to assist instructors and students to master the material and apply it to their areas of interest, whether it be to further their studies in mathematics, science, engineering, statistics, economics, or other disciplines. Linear Algebra has very appealing features: •It is a solid axiomatic based mathematical theory that is accessible to a large variety of students. •It has a multitude of applications from many different fields, ranging from traditional science and engineering applications to more ‘daily life’ applications. •It easily allows for numerical experimentation through the use of a variety of readily available software (both commercial and open source). Several suggestions of different software are made. While MATLAB is certainly still a favorite choice, open-source programs such as Sage (especially among algebraists) and the Python libraries are increasingly popular. This text guides the student to try out different programs by providing specific commands.

Download or read book AI Mastery Trilogy written by Andrew Hinton and published by Book Bound Studios. This book was released on with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the "AI Mastery Trilogy," the ultimate collection for professionals seeking to conquer the world of artificial intelligence (AI). This 3-in-1 compendium is meticulously crafted to guide you from the foundational principles of AI to the intricate mathematical frameworks and practical coding applications that will catapult your expertise to new heights. Book 1: "AI Basics for Managers" by Andrew Hinton is your gateway to understanding and implementing AI in business. It equips managers with the knowledge to navigate the AI landscape, identify opportunities, and lead their organizations toward a future of innovation and growth. Book 2: "Essential Math for AI" demystifies the mathematical backbone of AI, offering a deep dive into the core concepts that fuel AI systems. From linear algebra to game theory, this book is a treasure trove for anyone eager to grasp the numerical and logical foundations that underpin AI's transformative power. Book 3: "AI and ML for Coders" is the hands-on manual for coders ready to harness AI and machine learning in their projects. It provides a comprehensive overview of AI and ML technologies, practical coding advice, and ethical considerations, ensuring you're well-equipped to create cutting-edge, responsible AI applications. The "AI Mastery Trilogy" is more than just a set of books; it's a comprehensive learning journey designed to empower business leaders, mathematicians, and coders alike. Whether you're looking to lead, understand, or build the future of AI, this collection is an indispensable resource for mastering the art and science of one of the most exciting fields in technology. Embrace the AI revolution and secure your copy of the "AI Mastery Trilogy" today!

Download or read book Practical Linear Algebra for Machine Learning written by Amirsina Torfi and published by . This book was released on 2019-12-26 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning is everywhere these days and a lot of fellows desire to learn it and even master it! This burning desire creates a sense of impatience. We are looking for shortcuts and willing to ONLY jump to the main concept. If you do a simple search on the web, you see thousands of people asking "How can I learn Machine Learning?", "What is the fastest approach to learn Machine Learning?", and "What are the best resources to start Machine Learning?" \textit. Mastering a branch of science is NOT just a feel-good exercise. It has its own requirements.One of the most critical requirements for Machine Learning is Linear Algebra. Basically, the majority of Machine Learning is working with data and optimization. How can you want to learn those without Linear Algebra? How would you process and represent data without vectors and matrices? On the other hand, Linear Algebra is a branch of mathematics after all. A lot of people trying to avoid mathematics or have the temptation to "just learn as necessary." I agree with the second approach, though. \textit: You cannot escape Linear Algebra if you want to learn Machine Learning and Deep Learning. There is NO shortcut.The good news is there are numerous resources out there. In fact, the availability of numerous resources made me ponder whether writing this book was necessary? I have been blogging about Machine Learning for a while and after searching and searching I realized there is a deficiency of an organized book which \textbf teaches the most used Linear Algebra concepts in Machine Learning, \textbf provides practical notions using everyday used programming languages such as Python, and \textbf be concise and NOT unnecessarily lengthy.In this book, you get all of what you need to learn about Linear Algebra that you need to master Machine Learning and Deep Learning.

Download or read book Mastering Linear Algebra written by Mahesh Kumar and published by . This book was released on 2024-01-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear Algebra: The Secret Mathematics of the Universe The world around us operates on a game of numbers - from the orbits of planets in the sky to the balance in our bank accounts, everything is powered by numbers. But along with numbers, there is another powerful tool: linear algebra. This mathematics gives us the ability to identify the hidden patterns of things around us, understand their behavior, and make accurate predictions. So what exactly is linear algebra? In simple terms, it is the study of mathematical operations and relationships that occur in the world of vectors, not numbers. A vector is a line with both magnitude and direction. Linear algebra performs operations on these vectors, explores their properties, and understands the relationships between them. But linear algebra is not just involved in the game of vectors. It plays important roles in our everyday lives and in various fields. For example: - Physics: Linear algebra is used to model the motion of objects, the behavior of waves, and the structure of matter. - Engineering: Linear algebra is used to design structures, analyze systems, and optimize processes. - Computer science: Linear algebra is used to develop algorithms, solve problems, and visualize data. - Economics: Linear algebra is used to model markets, analyze data, and make predictions. - Statistics: Linear algebra is used to analyze data, make predictions, and test hypotheses. Linear algebra is a powerful tool that can be used to understand and analyze the world around us. It is a fundamental concept in mathematics and science, and it has a wide range of applications. Here are some additional details that may be helpful: - The word "linear" means "straight" or "straightforward." In linear algebra, we study the behavior of objects that move in a straight line or along a straight path. - The word "algebra" means "the art of solving equations." In linear algebra, we use equations to describe the relationships between vectors.

Download or read book Data Science and Machine Learning Series written by Advait Jayant and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Become proficient in linear algebra in this course in the Data Science and Machine Learning Series. Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on to practice applying linear algebra concepts. Also here are all of Advait Jayant's highly-rated videos on O'Reilly, including the full Data Science and Machine Learning Series . The following ten topics will be covered in this Data Science and Machine Learning course: Introducing Linear Algebra . Understand linear algebra concepts in this first topic in the Data Science and Machine Learning Series. Linear algebra is a continuous form of mathematics that allows us to model natural phenomena and compute them efficiently. Functional analysis is the application of linear algebra to spaces of functions. Be able to explain vectors which are ordered lists of numbers. Perform vector addition and multiplication. Creating Linear Transformations, Span, and Basis Vectors . Create linear transformations, span, and basis vectors in this second topic within this linear algebra course in the Data Science and Machine Learning Series. Using Linear Transformations and Matrices . Use linear transformations and matrices in this third topic within this linear algebra course in the Data Science and Machine Learning Series. See how linear transformations look in two dimensions and practice more advanced vector multiplication. Using Linear Transformations as Composition . Use linear transformations as composition in this fourth topic within this linear algebra course in the Data Science and Machine Learning Series. Practice matrix multiplication as composition including the use of the Shear Transformation. Apply transformations in a particular sequence. Creating Matrix Determinants . Create matrix determinants in this fifth topic within this linear algebra course in the Data Science and Machine Learning Series. The determinant is the scaling factor by which a linear transformation changes the area of any shape. Mastering Inverse Matrices, Linear Systems of Equations, Rank, Column Spaces, and Null Spaces . Master inverse matrices, linear systems of equations, rank, column spaces, and Null Spaces in this sixth topic within this linear algebra course in the Data Science and Machine Learning Series. Using Dot Products and Duality . Know all about dot products and duality in this seventh topic within this linear algebra course in the Data Science and Machine Learning Series....

Download or read book Linear Algebra and Its Applications Books a la Carte Edition Plus Mymathlab with Pearson Etext Access Code Card written by David C. Lay and published by Pearson College Division. This book was released on 2014-12-29 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: NOTE: This edition features the same content as the traditional text in a convenient, three-hole-punched, loose-leaf version. Books a la Carte also offer a great value--this format costs significantly less than a new textbook. Before purchasing, check with your instructor or review your course syllabus to ensure that you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for each title, including customized versions for individual schools, and registrations are not transferable. In addition, you may need a CourseID, provided by your instructor, to register for and use Pearson's MyLab & Mastering products. xxxxxxxxxxxxxxx For courses in linear algebra. This package includes MyMathLab®. With traditional linear algebra texts, the course is relatively easy for students during the early stages as material is presented in a familiar, concrete setting. However, when abstract concepts are introduced, students often hit a wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations) are not easily understood and require time to assimilate. These concepts are fundamental to the study of linear algebra, so students' understanding of them is vital to mastering the subject. This text makes these concepts more accessible by introducing them early in a familiar, concrete Rn setting, developing them gradually, and returning to them throughout the text so that when they are discussed in the abstract, students are readily able to understand. Personalize learning with MyMathLab MyMathLab is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. MyMathLab includes assignable algorithmic exercises, the complete eBook, interactive figures, tools to personalize learning, and more.

Download or read book Machine Learning Mastery Deep Learning and Natural Language Processing Integration written by Dr.Talluri.Sunil Kumar and published by SK Research Group of Companies. This book was released on 2024-07-24 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr.Talluri.Sunil Kumar, Professor, Department of CSE-(CyS, DS) and AI&DS, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India. Dr.Sagar Yeruva, Associate Professor, Department of CSE - AIML & IoT, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India.

Download or read book Linear Algebra with Applications written by Steven J. Leon and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edition features the same content as the traditional text in a convenient, three-hole-punched, loose-leaf version. Books a la Carte also offer a great value-this format costs significantly less than a new textbook. Before you purchase, check with your instructor or review your course syllabus to ensure that you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for each title, including customized versions for individual schools, and registrations are not transferable. In addition, you may need a CourseID, provided by your instructor, to register for and use Pearson's MyLab & Mastering products. This book is for sophomore-level or junior/senior-level first courses in linear algebra and assumes calculus as a prerequisite. This thorough and accessible text, from one of the leading figures in the use of technology in linear algebra, gives students a challenging and broad understanding of the subject. The author infuses key concepts with their modern practical applications to offer students examples of how mathematics is used in the real world. Each chapter contains integrated worked examples and chapter tests. The book stresses the important roles geometry and visualization play in understanding linear algebra.