Machine learning

This book describes how human hearing works and how to build machines that analyze sounds in the same way that people do.

This book describes how human hearing works and how to build machines that analyze sounds in the same way that people do.

Discover all-practical implementations of the key algorithms and models for handling unlabeled data.

The next generation of AI is hereuse it to lead your business forward.

Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks.

While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math.

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data.

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data.

If you want to work in any computational or technical field, you need to understand linear algebra.

If you want to work in any computational or technical field, you need to understand linear algebra.

Candlesticks have become a key component of platforms and charting programs for financial trading.

Candlesticks have become a key component of platforms and charting programs for financial trading.

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production.

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production.

The design patterns in this book capture best practices and solutions to recurring problems in machine learning.

Is Kubernetes ready for stateful workloads?

Is Kubernetes ready for stateful workloads?

Machine learning systems are both complex and unique.

Machine learning systems are both complex and unique.

Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization.

Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks.

Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks.

Master the math needed to excel in data science, machine learning, and statistics.

Master the math needed to excel in data science, machine learning, and statistics.

Data is a fantastic raw resource for powering change in an organization, but all too often the people working in those organizations don't have the necessary skills to communicate with data effectively.

Data is a fantastic raw resource for powering change in an organization, but all too often the people working in those organizations don't have the necessary skills to communicate with data effectively.

Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error.

Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error.

This book provides a thorough exploration of the intersection between gender-based healthcare disparities and the transformative potential of artificial intelligence (AI) and machine learning (ML).

Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.

Bridging the gap between theoretical asset pricing and industry practices in factors and factor investing, Zhang et al.

This unified random matrix approach to large-dimensional machine learning covers applications from power detection to deep neural networks.

This Element analyses real-world trial courts data with state-of-the-art machine learning and causal inference methods.

This Element analyses real-world trial courts data with state-of-the-art machine learning and causal inference methods.

This book provides an overview of the Internet of Things Network and Machine Learning and introduces Internet of Things architecture.

A how-to guide and scientific tutorial covering the universe of reinforcement learning and control theory for online decision making.

Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.

Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data.

Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals.

Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals.

The popularity of Android mobile phones has caused more cybercriminals to create malware applications that carry out various malicious activities.

The popularity of Android mobile phones has caused more cybercriminals to create malware applications that carry out various malicious activities.

The growing popularity of advanced multimedia-rich applications along with the increasing affordability of high-end smart mobile devices has led to a massive growth in mobile data traffic that puts significant pressure on the underlying network technology.

This book provides a systematic study of the Programme for International Student Assessment (PISA) based on big data analysis, aiming to examine the contextual factors relevant to students' digital reading performance.

This book addresses issues related to the integration of digital evolutionary technologies and provides solutions to various challenges encountered during the implementation process.

This book addresses issues related to the integration of digital evolutionary technologies and provides solutions to various challenges encountered during the implementation process.

This book presents Internet of Things (IoT) technology and security-related solutions that employ intelligent data processing technologies and machine learning (ML) approaches for data analytics.

This book presents Internet of Things (IoT) technology and security-related solutions that employ intelligent data processing technologies and machine learning (ML) approaches for data analytics.

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning.

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning.