Calculus For Machine Learning
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Calculus For Machine Learning
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2022-02-23
Calculus For Machine Learning written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-23 with Computers categories.
Calculus seems to be obscure, but it is everywhere. In machine learning, while we rarely write code on differentiation or integration, the algorithms we use have theoretical roots in calculus. If you ever wondered how to understand the calculus part when you listen to people explaining the theory behind a machine learning algorithm, this new Ebook, in the friendly Machine Learning Mastery style that you’re used to, is all you need. Using clear explanations and step-by-step tutorial lessons, you will understand the concept of calculus, how it is relates to machine learning, what it can help us on, and much more.
Mathematics Of Machine Learning
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Author : Tivadar Danka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-05-30
Mathematics Of Machine Learning written by Tivadar Danka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-30 with Computers categories.
Build a solid foundation in the core math behind machine learning algorithms with this comprehensive guide to linear algebra, calculus, and probability, explained through practical Python examples Purchase of the print or Kindle book includes a free PDF eBook Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Master linear algebra, calculus, and probability theory for ML Bridge the gap between theory and real-world applications Learn Python implementations of core mathematical concepts Book DescriptionMathematics of Machine Learning provides a rigorous yet accessible introduction to the mathematical underpinnings of machine learning, designed for engineers, developers, and data scientists ready to elevate their technical expertise. With this book, you’ll explore the core disciplines of linear algebra, calculus, and probability theory essential for mastering advanced machine learning concepts. PhD mathematician turned ML engineer Tivadar Danka—known for his intuitive teaching style that has attracted 100k+ followers—guides you through complex concepts with clarity, providing the structured guidance you need to deepen your theoretical knowledge and enhance your ability to solve complex machine learning problems. Balancing theory with application, this book offers clear explanations of mathematical constructs and their direct relevance to machine learning tasks. Through practical Python examples, you’ll learn to implement and use these ideas in real-world scenarios, such as training machine learning models with gradient descent or working with vectors, matrices, and tensors. By the end of this book, you’ll have gained the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements. *Email sign-up and proof of purchase requiredWhat you will learn Understand core concepts of linear algebra, including matrices, eigenvalues, and decompositions Grasp fundamental principles of calculus, including differentiation and integration Explore advanced topics in multivariable calculus for optimization in high dimensions Master essential probability concepts like distributions, Bayes' theorem, and entropy Bring mathematical ideas to life through Python-based implementations Who this book is for This book is for aspiring machine learning engineers, data scientists, software developers, and researchers who want to gain a deeper understanding of the mathematics that drives machine learning. A foundational understanding of algebra and Python, and basic familiarity with machine learning tools are recommended.
Math For Deep Learning
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Author : Ronald T. Kneusel
language : en
Publisher: No Starch Press
Release Date : 2021-12-07
Math For Deep Learning written by Ronald T. Kneusel and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.
Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
Introduction To Deep Learning
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Author : Sandro Skansi
language : en
Publisher: Springer
Release Date : 2018-02-04
Introduction To Deep Learning written by Sandro Skansi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-04 with Computers categories.
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
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.
Calculus For Machine Learning
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Author : BIMAL. KUJUR
language : en
Publisher: Independently Published
Release Date : 2025-02-15
Calculus For Machine Learning written by BIMAL. KUJUR and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-15 with categories.
This book is designed to bridge the gap between the mathematical foundations of calculus and their practical applications in the rapidly evolving field of machine learning (ML). Whether you are a student, a researcher, or a practitioner, this book aims to provide you with a comprehensive understanding of how calculus underpins many of the algorithms and techniques that drive modern ML. The Intersection of Calculus and Machine Learning Machine learning has transformed the way we approach data, enabling us to build models that can learn from and make predictions on complex datasets. At the heart of many ML algorithms lies calculus, the branch of mathematics that deals with rates of change and accumulation. From optimizing loss functions to training neural networks, calculus provides the tools necessary to understand and improve these models. This book is structured to take you on a journey from the fundamental concepts of calculus to their advanced applications in ML. We begin with a review of essential calculus topics, ensuring that readers have a solid foundation. We then delve into more specialized areas, such as gradient descent, backpropagation, and optimization techniques, illustrating how these concepts are applied in real-world ML problems.
A Guide To Applied Machine Learning For Biologists
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Author : Mohammad "Sufian" Badar
language : en
Publisher: Springer Nature
Release Date : 2023-06-21
A Guide To Applied Machine Learning For Biologists written by Mohammad "Sufian" Badar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-21 with Science categories.
This textbook is an introductory guide to applied machine learning, specifically for biology students. It familiarizes biology students with the basics of modern computer science and mathematics and emphasizes the real-world applications of these subjects. The chapters give an overview of computer systems and programming languages to establish a basic understanding of the important concepts in computer systems. Readers are introduced to machine learning and artificial intelligence in the field of bioinformatics, connecting these applications to systems biology, biological data analysis and predictions, and healthcare diagnosis and treatment. This book offers a necessary foundation for more advanced computer-based technologies used in biology, employing case studies, real-world issues, and various examples to guide the reader from the basic prerequisites to machine learning and its applications.
Calculus With Python For Data Science And Machine Learning
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Author : Hayden Van Der Post
language : en
Publisher: Independently Published
Release Date : 2025-11-28
Calculus With Python For Data Science And Machine Learning written by Hayden Van Der Post and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-28 with Computers categories.
Reactive Publishing Modern data science and machine learning run on a mathematical engine: calculus. If you understand how functions behave, how gradients move, and how optimization algorithms learn, you gain a decisive advantage over practitioners who treat models as black boxes. This book shows you that engine with clarity, structure, and real Python implementations. Calculus with Python for Data Science and Machine Learning takes you from foundational concepts to the core mathematical tools used in today's modeling pipelines. Rather than drowning you in abstract proofs, it focuses on how calculus shapes algorithms, informs decisions, and improves model performance. You'll learn why gradients matter, how optimization works, and how mathematical structure drives learning in real systems. Each chapter connects theory to practical Python examples, allowing you to visualize concepts, manipulate functions, and build intuition that transfers directly into machine learning workflows. Inside, you'll master: - Derivatives, slopes, and rates of change for modeling and prediction - Integrals for probability, expectations, and distribution behavior - Multivariable calculus for models with many parameters - Gradient descent, learning rates, momentum, and optimization logic - Jacobians, Hessians, and curvature for advanced ML diagnostics - Calculus-driven intuition behind loss functions and regularization - How Python visualizations reveal model structure and decision boundaries - The math powering linear regression, logistic models, neural networks, and more This book teaches you how to think mathematically about machine learning. You'll understand what models are doing, why they behave the way they do, and how to refine them with precision. Whether you're building your first ML pipeline or advancing toward deeper quantitative work, this is the essential bridge between mathematics, code, and real-world modeling. If you want to elevate your data science and machine learning skills through the power of calculus, this book gives you the clearest path forward.
Math For Deep Learning
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Author : Ronald Kneusel
language : en
Publisher:
Release Date : 2021
Math For Deep Learning written by Ronald Kneusel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Deep learning is everywhere, making this powerful driver of AI something more STEM professionals need to know. Learning which library commands to use is one thing, but to truly understand the discipline, you need to grasp the mathematical concepts that make it tick. This book will give you a working knowledge of topics in probability, statistics, linear algebra, and differential calculus - the essential math needed to make deep learning comprehensible, which is key to practicing it successfully. Each of the four subfields are contextualized with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. Chapters build upon one another, with foundational topics such as Bayes' theorem followed by more advanced concepts, like training neural networks using vectors, matrices, and derivatives of functions. You'll ultimately put all this math to use as you explore and implement deep learning algorithms, including backpropagation and gradient descent - the foundational algorithms that have enabled the AI revolution. You'll learn: •The rules of probability, probability distributions, and Bayesian probability •The use of statistics for understanding datasets and evaluating models •How to manipulate vectors and matrices, and use both to move data through a neural network •How to use linear algebra to implement principal component analysis and singular value decomposition •How to apply improved versions of gradient descent, like RMSprop, Adagrad and Adadelta Once you understand the core math concepts presented throughout this book through the lens of AI programming, you'll have foundational know-how to easily follow and work with deep learning.
Artificial Intelligence And Machine Learning
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Author : Mr. Ajeet Singh
language : en
Publisher: RK Publication
Release Date : 2024-10-08
Artificial Intelligence And Machine Learning written by Mr. Ajeet Singh and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-08 with Computers categories.
Artificial Intelligence and Machine Learning the foundational concepts, techniques, and applications of AI and ML. The key topics such as supervised and unsupervised learning, neural networks, natural language processing, and deep learning. It emphasizes the practical integration of AI and ML across various industries, providing insights into real-world problem-solving. With accessible explanations and examples, it serves as both an introduction and a guide for those looking to understand and apply these transformative technologies in diverse fields.