Math For Machine Learning
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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.
Fundamental Mathematical Concepts For Machine Learning In Science
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Author : Umberto Michelucci
language : en
Publisher: Springer Nature
Release Date : 2024-05-16
Fundamental Mathematical Concepts For Machine Learning In Science written by Umberto Michelucci and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-16 with Mathematics categories.
This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplines—such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating machine learning effectively into their research. Numerous texts delve into the technical execution of machine learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. For instance, a firm grasp of calculus is imperative to comprehend the training processes of algorithms and neural networks, while linear algebra is essential for the application and efficient training of various algorithms, including neural networks. Absent a solid mathematical base, machine learning applications may be, at best, cursory, or at worst, fundamentally flawed. This book lays the foundation for a comprehensive understanding of machine learning algorithms and approaches.
Algorithmic Mathematics In Machine Learning
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Author : Bastian Bohn
language : en
Publisher: SIAM
Release Date : 2024-04-08
Algorithmic Mathematics In Machine Learning written by Bastian Bohn and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-08 with Computers categories.
This unique book explores several well-known machine learning and data analysis algorithms from a mathematical and programming perspective. The authors present machine learning methods, review the underlying mathematics, and provide programming exercises to deepen the reader’s understanding; accompany application areas with exercises that explore the unique characteristics of real-world data sets (e.g., image data for pedestrian detection, biological cell data); and provide new terminology and background information on mathematical concepts, as well as exercises, in “info-boxes” throughout the text. Algorithmic Mathematics in Machine Learning is intended for mathematicians, computer scientists, and practitioners who have a basic mathematical background in analysis and linear algebra but little or no knowledge of machine learning and related algorithms. Researchers in the natural sciences and engineers interested in acquiring the mathematics needed to apply the most popular machine learning algorithms will also find this book useful. This book is appropriate for a practical lab or basic lecture course on machine learning within a mathematics curriculum.
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.
Math For Machine Learning
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Author : Richard Han
language : en
Publisher:
Release Date : 2018-07-12
Math For Machine Learning written by Richard Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-12 with Mathematics categories.
This book explains the math behind machine learning using simple but concrete examples. This book will get you started in machine learning in a smooth and natural way, preparing you for more advanced topics and dispelling the belief that machine learning is complicated, difficult, and intimidating.
Math For Machine Learning
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Author : Richard Han
language : en
Publisher:
Release Date : 2018
Math For Machine Learning written by Richard Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"Would you like to learn a mathematics subject that is crucial for many high-demand lucrative career fields such as: Computer Science, Data Science. Artificial Intelligence. If you're looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you. Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. You intend to pursue a masters degree or PhD, and machine learning is a required or recommended subject. Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have created many successful online math courses that students around the world have found invaluable--courses in linear algebra, discrete math, and calculus."--Resource description page.
Hands On Mathematics For Deep Learning
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Author : Jay Dawani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-06-12
Hands On Mathematics For Deep Learning written by Jay Dawani 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 2020-06-12 with Computers categories.
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
Math Application
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Author : Vincenza Nowell
language : en
Publisher:
Release Date : 2021-03-25
Math Application written by Vincenza Nowell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-25 with categories.
Are you interested in learning about the amazing capabilities of machine learning, but you're worried it will be just too complicated? Machine learning is an incredible technology which we're only just beginning to understand. This guide breaks down the fundamentals of machine learning in a way that anyone can understand. About the different kinds of machine learning models, neural networks, and the way these models learn data, you'll find everything you need to know to get started with machine learning in a concise, easy-to-understand way.
Machine Learning Math
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Author :
language : en
Publisher:
Release Date : 2020-05-21
Machine Learning Math written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-21 with categories.
Are you looking for a complete guide of machine learning? Then keep reading... In this book, you will learn about the OpenAI Gym, used in reinforcement learning projects with several examples of the training platform provided out of the box. Machine Learning Math is the book most readers will want to have when starting to learn machine learning. This book is a reference, something you can keep coming back to hence suitable for newbies. The book is perfect for all people who have a desire to study data science. Have you heard of machine learning being everywhere, and you intend to understand what it can do? Or are you familiar with applying the tools of machine learning, but you want to make sure you aren't missing any? Having a little knowledge about mathematics, statistics, and probability would be helpful, but this book has been written in such a way that you will get most of this knowledge as you continue reading. You should not shy away from reading the book if you have no background in machine learning. You will learn how to use reinforcement learning algorithms in other tasks, for example, the board game Go, and generating deep image classifiers. This will help you to get a comprehensive understanding of reinforcement learning and help you solve real-world problems. The most interesting part of this book is the asynchronous reinforcement learning framework. You will learn what the shortcomings of DQN are, and why DQN is challenging to apply in complex tasks. Then, you will learn how to apply the asynchronous reinforcement learning framework in the actor-critic method REINFORCE, which led us to the A3C algorithm. You will learn four important things. The first one is how to implement games using gym and how to play games for relaxation and having fun. The second one is that you will learn how to preprocess data in reinforcement learning tasks such as in computer games. For practical machine learning applications, you will spend a great deal of time understanding and refining data, which affects the performance of an AI system a lot. The third one is the deep Q-learning algorithm. You will learn the intuition behind it, for example, why the replay memory is necessary, why the target network is needed, where the update rule comes from, and so on. The final one is that you will learn how to implement DQN using TensorFlow and how to visualize the training process. The following is a glimpse of what you will find inside the book: Introduction to machine learning The best machine learning algorithms Regression (a problem of predicting a real-valued label) and classification( a problem of automatically assigning a label to unlabeled example-for example spam detection) Reinforcement learning Robotics Supervised and Unsupervised learning How to implement a convolutional neural network(usually used for images) in TensorFlow Deep Learning Data preparation and processing TensorFlow machine learning frameworks Neural Networks (a combination of linear and non-linear functions) Clustering(aims to group similar samples together) Even if you have never studied Machine Learning before, you can learn it quickly. So what are you waiting for? Go to the top of the page and click Buy Now!
Essential Math For Data Science
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Author : Thomas Nield
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-05-26
Essential Math For Data Science written by Thomas Nield and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-26 with Computers categories.
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market