Mathematics Archives

Before Machine Learning Volume 3 – Probability and Statistics for A.I: The fundamental mathematics for Data Science and Artificial Intelligence

Price: ~~$5.99~~ – $16.99
(as of Dec 15,2024 17:39:31 UTC – Details)

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Subject
Linear Algebra
Calculus
Probability and Statistics

ASIN ‏ : ‎ B0DGF5T8SF
Publication date ‏ : ‎ September 7, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 11101 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Not enabled
Enhanced typesetting ‏ : ‎ Not Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 400 pages
Format ‏ : ‎ Print Replica

In this post, we will be exploring the fundamental mathematics behind Data Science and Artificial Intelligence – Probability and Statistics. Before diving into the world of Machine Learning, it is essential to have a solid understanding of these two fields as they form the backbone of many algorithms and techniques used in the industry.

Probability theory is the study of random events and the likelihood of different outcomes occurring. In the context of Data Science and A.I, probability theory is used to model uncertainty and make predictions based on available data. Understanding concepts such as random variables, probability distributions, and conditional probability is crucial for building accurate and reliable models.

Statistics, on the other hand, is the practice of collecting, analyzing, and interpreting data to make informed decisions. In Data Science and A.I, statistics plays a vital role in hypothesis testing, parameter estimation, and model evaluation. Key concepts such as descriptive statistics, inferential statistics, and regression analysis are essential for deriving meaningful insights from data.

By mastering Probability and Statistics, aspiring Data Scientists and A.I professionals can gain a deeper understanding of how algorithms work and how to interpret their results. These foundational principles provide the necessary tools to tackle complex problems and develop innovative solutions in the field of Artificial Intelligence.

Stay tuned for our upcoming posts in the Before Machine Learning series, where we will delve deeper into the mathematical concepts that drive the cutting-edge technologies of Data Science and Artificial Intelligence.
#Machine #Learning #Volume #Probability #Statistics #A.I #fundamental #mathematics #Data #Science #Artificial #Intelligence

December 15, 2024

Mathematics of Deep Learning: An Introduction (De Gruyter Textbook)

Price: ~~$65.99~~ – $40.47
(as of Dec 15,2024 17:38:25 UTC – Details)

Publisher ‏ : ‎ De Gruyter; 1st edition (April 27, 2023)
Language ‏ : ‎ English
Perfect Paperback ‏ : ‎ 132 pages
ISBN-10 ‏ : ‎ 3111024318
ISBN-13 ‏ : ‎ 978-3111024318
Item Weight ‏ : ‎ 8 ounces
Dimensions ‏ : ‎ 7 x 0.25 x 10 inches

The field of deep learning has been rapidly growing in recent years, with applications in a wide range of fields such as computer vision, natural language processing, and robotics. However, understanding the mathematical principles behind deep learning can be challenging for many students and practitioners.

In our new textbook, “Mathematics of Deep Learning: An Introduction,” published by De Gruyter, we aim to provide a comprehensive overview of the mathematical foundations of deep learning. This book is designed for students and researchers who are interested in gaining a deeper understanding of the underlying principles of deep learning algorithms.

The book covers a wide range of topics, including linear algebra, calculus, probability theory, and optimization, all of which are essential for understanding how deep learning models work. Each chapter includes detailed explanations, examples, and exercises to help readers grasp the key concepts and techniques.

Whether you are a student looking to learn more about the mathematical aspects of deep learning or a practitioner seeking to enhance your knowledge and skills, “Mathematics of Deep Learning: An Introduction” will provide you with the necessary tools to navigate the complex world of deep learning algorithms.

Get your copy of “Mathematics of Deep Learning: An Introduction” today and unlock the power of mathematics in deep learning!
#Mathematics #Deep #Learning #Introduction #Gruyter #Textbook

December 15, 2024

Before Machine Learning Volume 2 – Calculus for A.I: The fundamental mathematics for Data Science and Artificial Intelligence

Price: $9.99
(as of Dec 13,2024 06:39:50 UTC – Details)

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Subject
Linear Algebra Calculus Probability and Statistics

ASIN ‏ : ‎ B0CQ8V3RMW
Publication date ‏ : ‎ December 13, 2023
Language ‏ : ‎ English
File size ‏ : ‎ 7588 KB
Text-to-Speech ‏ : ‎ Not enabled
Enhanced typesetting ‏ : ‎ Not Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Format ‏ : ‎ Print Replica

In this post, we will delve into the fundamental mathematics that serve as the foundation for Data Science and Artificial Intelligence – Calculus.

Calculus is a branch of mathematics that deals with rates of change and accumulation. It is an essential tool for understanding how systems evolve over time, making it a crucial component of machine learning algorithms.

In the context of Data Science and Artificial Intelligence, calculus is used to optimize models, compute gradients, and understand the behavior of complex systems. By mastering the concepts of calculus, data scientists and AI researchers can gain a deeper insight into the underlying mechanisms of machine learning algorithms.

In this post, we will cover topics such as derivatives, integrals, optimization, and differential equations – all of which play a crucial role in the development of advanced AI systems. We will also discuss how calculus is applied in various fields of data science, such as deep learning, computer vision, and natural language processing.

So, if you want to take your understanding of machine learning to the next level, join us on this journey through the world of calculus for A.I. Stay tuned for Before Machine Learning Volume 2 – Calculus for A.I!
#Machine #Learning #Volume #Calculus #A.I #fundamental #mathematics #Data #Science #Artificial #Intelligence

December 13, 2024

AI Mastery Trilogy: A Comprehensive Guide to AI Basics for Managers, Essential Mathematics for AI, and Coding Practices for Modern Programmers in the AI Era (3-in-1 Collection) (AI Fundamentals)

Price: $4.99
(as of Dec 04,2024 15:42:22 UTC – Details)

From the Publisher

ASIN ‏ : ‎ B0CRPS5CCV
Publisher ‏ : ‎ Book Bound Studios (January 5, 2024)
Publication date ‏ : ‎ January 5, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 13325 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 480 pages
Page numbers source ISBN ‏ : ‎ 176159009X

Customers say

Customers find the book insightful and educational. They say it provides clear explanations and step-by-step walkthroughs for programmers. Readers describe the collection as exceptional, extensive, and well organized. They also mention it’s a great read and applicable to real-world scenarios.

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Are you a manager looking to gain a solid understanding of AI basics? Are you a programmer wanting to enhance your coding skills for the modern AI era? Look no further than the AI Mastery Trilogy, a comprehensive guide that covers everything you need to know to excel in the world of artificial intelligence.

In the first book, “AI Basics for Managers,” you will learn the fundamentals of AI, including its history, applications, and potential impact on industries. This book is perfect for managers who want to stay ahead of the curve and understand how AI can benefit their organization.

The second book, “Essential Mathematics for AI,” delves into the mathematical concepts that underpin artificial intelligence. From linear algebra to calculus, this book covers all the essential math skills you need to excel in AI. Whether you’re a beginner or an experienced programmer, this book will help you understand the mathematical principles behind AI algorithms.

Finally, in the third book, “Coding Practices for Modern Programmers in the AI Era,” you will learn the best coding practices for developing AI applications. From Python to TensorFlow, this book covers the tools and techniques you need to write efficient and scalable AI code. Whether you’re a seasoned programmer or just starting out, this book will help you master the coding skills needed in the AI era.

Don’t miss out on this 3-in-1 collection that covers AI basics, essential mathematics, and coding practices for modern programmers. Get your copy of the AI Mastery Trilogy today and take your AI skills to the next level!
#Mastery #Trilogy #Comprehensive #Guide #Basics #Managers #Essential #Mathematics #Coding #Practices #Modern #Programmers #Era #3in1 #Collection #Fundamentals

December 4, 2024

Student Solutions Guide for Discrete Mathematics and Its – VERY GOOD

Student Solutions Guide for Discrete Mathematics and Its – VERY GOOD

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Looking for a comprehensive guide to help you navigate the world of Discrete Mathematics? Look no further! Our Student Solutions Guide for Discrete Mathematics is here to provide you with all the tools and resources you need to succeed in this challenging subject.

With step-by-step explanations, practice problems, and detailed solutions, this guide will help you master key concepts such as set theory, logic, graph theory, and more. Whether you’re a beginner or an advanced student, this guide is designed to meet you where you are and help you reach your academic goals.

Don’t let Discrete Mathematics intimidate you any longer. With our Student Solutions Guide, you’ll be well-equipped to tackle any problem that comes your way. Get your copy today and start excelling in this fascinating field of mathematics!
#Student #Solutions #Guide #Discrete #Mathematics #GOOD

December 2, 2024

Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems

Price: ~~$79.99~~ – $62.99
(as of Dec 01,2024 23:56:12 UTC – Details)

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O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

Publisher ‏ : ‎ O’Reilly Media; 1st edition (February 14, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 602 pages
ISBN-10 ‏ : ‎ 1098107632
ISBN-13 ‏ : ‎ 978-1098107635
Item Weight ‏ : ‎ 2.15 pounds
Dimensions ‏ : ‎ 7 x 1.3 x 9.1 inches

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Customers find the book’s information quality excellent, well-structured, and wonderful. They say it provides a useful and wonderful overview of math concepts that relate to various AI systems. Readers also appreciate the detailed and thorough writing on the subject.

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Artificial Intelligence (AI) has revolutionized the way we interact with technology, from virtual assistants like Siri to self-driving cars. However, behind the scenes, AI systems rely heavily on advanced mathematical concepts to function efficiently and effectively.

In order to develop and maintain successful AI systems, it is essential to have a solid understanding of next-level mathematics. Here are some key mathematical concepts that are crucial for AI development:

1. Linear Algebra: Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transformations. In AI, linear algebra is used to represent and manipulate data, such as images, text, and audio. Understanding linear algebra is essential for tasks like image recognition, natural language processing, and recommendation systems.

2. Calculus: Calculus is the study of change and motion, and it plays a crucial role in optimizing AI algorithms. Techniques like gradient descent, which is used to minimize errors in machine learning models, rely heavily on calculus. Understanding calculus is key to developing efficient and accurate AI systems.

3. Probability and Statistics: Probability and statistics are essential for making informed decisions in AI. From analyzing data to making predictions, probability and statistics help AI systems understand and interpret the world around them. Concepts like Bayes’ theorem and statistical hypothesis testing are commonly used in AI applications.

4. Optimization: Optimization is the process of finding the best solution to a problem, and it is a fundamental concept in AI. Techniques like convex optimization and stochastic optimization are used to improve the performance of AI algorithms. Understanding optimization is crucial for developing efficient and scalable AI systems.

By mastering these essential mathematical concepts, developers can build more efficient and successful AI systems. Whether you are working on computer vision, natural language processing, or reinforcement learning, a strong foundation in mathematics is key to pushing the boundaries of AI technology. Stay tuned for more insights on the intersection of math and AI!
#Essential #Math #NextLevel #Mathematics #Efficient #Successful #Systems

December 1, 2024

4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering: ICAIAME 2022 (Engineering Cyber-Physical Systems and Critical Infrastructures, 7)

Price: ~~$379.99~~ – $290.19
(as of Nov 27,2024 21:06:32 UTC – Details)

Publisher ‏ : ‎ Springer; 2023rd edition (May 27, 2023)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 781 pages
ISBN-10 ‏ : ‎ 3031319559
ISBN-13 ‏ : ‎ 978-3031319556
Item Weight ‏ : ‎ 2.79 pounds
Dimensions ‏ : ‎ 6.14 x 1.63 x 9.21 inches

We are thrilled to announce the 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2022) focusing on Engineering Cyber-Physical Systems and Critical Infrastructures.

Join us for this exciting conference where experts from around the world will come together to discuss the latest advancements in artificial intelligence and applied mathematics in engineering. The conference will cover a wide range of topics including machine learning, data analytics, optimization techniques, and more.

Don’t miss this opportunity to network with fellow researchers, industry professionals, and academics in the field. Stay tuned for more updates on keynote speakers, workshops, and paper submission guidelines.

Save the date for ICAIAME 2022 and be a part of this cutting-edge conference shaping the future of engineering cyber-physical systems and critical infrastructures. See you there! #ICAIAME2022 #ArtificialIntelligence #Engineering #Mathematics #CyberPhysicalSystems #CriticalInfrastructures
#4th #International #Conference #Artificial #Intelligence #Applied #Mathematics #Engineering #ICAIAME #Engineering #CyberPhysical #Systems #Critical #Infrastructures

November 27, 2024

The Foundational Mathematics of Artificial Intelligence

Price: $55.00
(as of Nov 24,2024 00:28:25 UTC – Details)

Publisher ‏ : ‎ Symmetry Seed Books (April 19, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 385 pages
ISBN-10 ‏ : ‎ 0997116323
ISBN-13 ‏ : ‎ 978-0997116328
Item Weight ‏ : ‎ 1.44 pounds
Dimensions ‏ : ‎ 6 x 0.87 x 9 inches

Artificial intelligence (AI) has become an increasingly important and prevalent technology in our modern world. From self-driving cars to virtual assistants, AI is revolutionizing various industries and changing the way we interact with technology.

At the core of AI lies mathematics. The foundational mathematics of artificial intelligence encompasses a wide range of mathematical concepts and techniques that are essential for understanding and developing AI systems.

One of the key mathematical concepts used in AI is linear algebra. Linear algebra is the branch of mathematics that deals with vector spaces and linear mappings between these spaces. In the context of AI, linear algebra is used to represent and manipulate data in the form of vectors and matrices. This allows AI systems to perform operations like matrix multiplication, which are essential for tasks such as image recognition and natural language processing.

Another important mathematical concept in AI is calculus. Calculus is the branch of mathematics that deals with rates of change and accumulation of quantities. In AI, calculus is used to optimize and fine-tune algorithms through techniques like gradient descent. This allows AI systems to learn from data and improve their performance over time.

Probability and statistics are also fundamental to AI. Probability theory is used to model uncertainty and make predictions about future events, while statistics is used to analyze and interpret data. In AI, probabilistic models like Bayesian networks are used to make decisions under uncertainty, while statistical techniques like hypothesis testing are used to evaluate the performance of AI systems.

Overall, the foundational mathematics of artificial intelligence is crucial for understanding how AI systems work and for developing new and improved AI technologies. By mastering concepts like linear algebra, calculus, probability, and statistics, researchers and developers can unlock the full potential of AI and create innovative solutions to complex problems.
#Foundational #Mathematics #Artificial #Intelligence

November 24, 2024

See It, Say It, Symbolize It: Teaching the Big Ideas in Elementary Mathematics (Develop a flexible and dynamic understanding of numbers and operations in young learners.)

Price: $26.49
(as of Nov 23,2024 04:20:12 UTC – Details)

ASIN ‏ : ‎ B0D6259XPC
Publisher ‏ : ‎ Solution Tree Press; 1st edition (June 4, 2024)
Publication date ‏ : ‎ June 4, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 15076 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Enabled

Teaching elementary mathematics can be a challenging task, but it doesn’t have to be intimidating. By implementing the “See It, Say It, Symbolize It” approach, educators can help young learners develop a flexible and dynamic understanding of numbers and operations.

The first step in this approach is to show students concrete examples of mathematical concepts. Whether using manipulatives like base-ten blocks or simply drawing out visuals, giving students something tangible to work with can help solidify their understanding of abstract ideas. Encourage students to not only see the math, but also talk about what they are seeing. This verbalization helps students internalize the concepts and make connections between different ideas.

Once students have a good grasp of the concrete examples, it’s time to move on to symbolic representations. This can include writing out equations, using mathematical symbols, or working with number sentences. By linking the concrete examples to symbolic representations, students can begin to see the connections between different ways of representing mathematical ideas.

By using the “See It, Say It, Symbolize It” approach, educators can help students develop a deep understanding of numbers and operations. This approach encourages students to think flexibly about math, making connections between different representations and building a strong foundation for future learning. With a solid understanding of numbers and operations, young learners can approach more complex mathematical concepts with confidence and ease.
#Symbolize #Teaching #Big #Ideas #Elementary #Mathematics #Develop #flexible #dynamic #understanding #numbers #operations #young #learners

November 23, 2024

Mathematics for Machine Learning

Price: $50.84
(as of Nov 22,2024 12:03:47 UTC – Details)

From the Publisher

Publisher ‏ : ‎ Cambridge University Press; 1st edition (April 23, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 398 pages
ISBN-10 ‏ : ‎ 110845514X
ISBN-13 ‏ : ‎ 978-1108455145
Item Weight ‏ : ‎ 1.76 pounds
Dimensions ‏ : ‎ 7 x 0.88 x 10 inches

Customers say

Customers find the book valuable, excellent, and best in class. Opinions are mixed on the math content, with some finding it a good reference for calculations and bringing out the beauty of mathematics, while others say it’s a poor textbook to study math beyond machine learning and hard to follow.

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Mathematics for Machine Learning: A Comprehensive Guide

Mathematics is the foundation of machine learning, providing the tools and techniques necessary to understand and manipulate data in order to make predictions and decisions. In this post, we will explore the key mathematical concepts that underpin machine learning and discuss how they are used in practice.

Linear Algebra: Linear algebra is a crucial mathematical tool in machine learning, used to represent and manipulate data in the form of vectors and matrices. Concepts such as matrix multiplication, eigenvalues and eigenvectors, and matrix decompositions are essential for tasks such as dimensionality reduction, feature extraction, and solving optimization problems.

Calculus: Calculus is another fundamental branch of mathematics that plays a key role in machine learning. Concepts such as derivatives and integrals are used to optimize machine learning models, calculate gradients for optimization algorithms, and understand the behavior of complex functions.

Probability and Statistics: Probability theory and statistics are essential for understanding uncertainty and making informed decisions in machine learning. Concepts such as probability distributions, hypothesis testing, and Bayesian inference are used to model and analyze data, estimate parameters, and make predictions with confidence intervals.

Optimization: Optimization is a critical mathematical technique in machine learning, used to find the best set of parameters for a given model. Techniques such as gradient descent, stochastic gradient descent, and convex optimization are used to train machine learning models and minimize the loss function.

Machine learning is a rapidly growing field that relies heavily on mathematics for its theoretical foundations and practical applications. By understanding the key mathematical concepts behind machine learning, you can develop a deeper insight into how algorithms work and how to apply them effectively in real-world scenarios. Whether you are a beginner looking to get started in machine learning or an experienced practitioner seeking to deepen your understanding, a solid grasp of mathematics is essential for success in this exciting and dynamic field.
#Mathematics #Machine #Learning

November 22, 2024

Tag: Mathematics

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