Fundamental Mathematical Concepts for Machine Learning in Science - Image 1

£53.87

Springer Fundamental Mathematical Concepts for Machine Learning in Science

Name: Fundamental Mathematical Concepts for Machine Learning in Science
Brand: Springer
Price: 53.87 GBP
Availability: InStock

AI & Machine Learning

Price History & Forecast

Last 22 days • 22 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 22 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £53 (17 days, 77.3%)

Price range: £53 - £54

Price levels: 2 different prices over 22 days

Description

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.

Key Features

Fundamental Mathematical Concepts for Machine Learning in Science

Product type: ABIS_BOOK

Brand: Springer

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 3031564308
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 17 May 2024
Listed Since: 10 February 2024

Barcode

No barcode data available

Springer Fundamental Mathematical Concepts for Machine Learning in Science

Price History & Forecast

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Fundamental Mathematical Concepts for Machine Learning in Science

Fundamentals of Machine Learning and Deep Learning in Medicine

Basic Mathematical Foundations of AI: Hands on with Python (Mastering Machine Learning)

A Mathematical Introduction to Data Science

Mathematical Foundations of Data Science (Texts in Computer Science)

Machine Learning

Machine Learning

Elements of Dimensionality Reduction and Manifold Learning

Machine Learning: The Basics (Machine Learning: Foundations, Methodologies, and Applications)

Mathematical Foundations for Deep Learning

Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning

Fundamentals of Machine Learning

Linear Algebra and Optimization for Machine Learning: A Textbook

Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications

Machine Learning Foundations: Volume 1: Supervised Learning

Machine Learning: A Practical Approach on the Statistical Learning Theory

A Greater Foundation for Machine Learning Engineering: The Hallmarks of the Great Beyond in Pytorch, R, Tensorflow, and Python

Machine Learning Methods

Mathematics for Machine Learning

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications)

Deep Learning: Foundations and Concepts

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics