Machine Learning

AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features,...

To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear...

A Sunday Times Business Book of the Year.Scary Smart will teach you how to navigate the scary and inevitable intrusion of Artificial Intelligence, with an accessible blueprint for creating a harmonious future alongside AI. From Mo Gawdat, the former...

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. If you're a data scientist or coder, this practical book -now revised...

Bridges the gap between AI and neuroscience by telling the story of how the brain came to be. 'I found this book amazing' Daniel Kahneman, Winner of the Nobel Prize in Economics and bestselling author of Thinking Fast & Slow The...

_______________'One of the best books yet written on data and algorithms. . .deserves a place on the bestseller charts.' (The Times) You are accused of a crime. Who would you rather determined your fate - a human or an algorithm?An algorithm is more...

Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders...

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it...

Incredibly useful, knowledge graphs help organizations keep track of medical research, cybersecurity threat intelligence, GDPR compliance, web user engagement, and much more. They do so by saving interlinked descriptions of entities (objects, events,...

Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse-a collection of R packages designed to...

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780262048644Author Simon J.D. PrinceFormat HardbackPage Count 544Imprint MIT PressPublisher MIT Press LtdWeight(grams) 1310gDimensions(mm) 236mm * 210mm *...

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate...

Bridges the gap between AI and neuroscience by telling the story of how the brain came to be. 'I found this book amazing' Daniel Kahneman, Winner of the Nobel Prize in Economics and bestselling author of Thinking Fast & Slow The...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780262049375Author Stefano V. AlbrechtFormat HardbackPage Count 394Imprint MIT PressPublisher MIT Press Ltd

The pressure to "be digital" has never been greater, but you can meet the challenge.The digital revolution is here, changing how work gets done, how industries are structured, and how people from all walks of life work, behave, and relate to each other...

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in...

With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems,...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780262035613Author Ian GoodfellowFormat HardbackPage Count 800Imprint MIT PressPublisher MIT Press LtdDimensions(mm) 229mm * 178mm * 25mm

As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets compliance requirements. Data governance incorporates the ways people,...

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image...

WINNER: Independent Press Awards 2023 - Category: Technology AI presents a new paradigm in software development, representing the biggest change to how we think about quality and testing in decades. Many of the well known issues around AI, such as...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780262046824Author Kevin P. MurphyFormat HardbackPage Count 944Imprint MIT PressPublisher MIT Press Ltd

Observability is critical for building, changing, and understanding the software that powers complex modern systems. Teams that adopt observability are much better equipped to ship code swiftly and confidently, identify outliers and aberrant behaviors,...

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In...

Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas Key Features Discover new...

We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and...

Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field...

Whether based on academic theories or machine learning strategies, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of...

This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780262048439Author Kevin P. MurphyFormat HardbackPage Count 1360Imprint MIT PressPublisher MIT Press Ltd

One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex...

Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. With this book, data scientists from the Python and R communities will learn how to...

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical...

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build...

'An excellent starter for those who want to gain an insight into how AI works and why it's likely to shape our lives.' - The Daily Telegraph Artificial intelligence will shake up our lives as thoroughly as the arrival of the internet. This popular,...

The go-to guide in machine learning projects from design to production. No ML skills required! In Managing Machine Learning Projects, you will learn essential machine learning project management techniques, including: Understanding an ML project's...

Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide As the most popular cloud service in the world today, Amazon Web Services offers a wide range of...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781999579579Author Andriy BurkovFormat PaperbackPage Count 310Imprint True Positive Inc.Publisher True Positive Inc.Weight(grams) 535gDimensions(mm) 235mm *...

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian...