Introducing Mlops: How to Scale Machine Learning in the Enterprise (Treveil Mark)(Paperback)

Introducing Mlops: How to Scale Machine Learning in the Enterprise (Treveil Mark)(Paperback)

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't

Practical Mlops: Operationalizing Machine Learning Models (Gift Noah)(Paperback)

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 differs from DevOps) and shows you how to

Introducing Machine Learning (Esposito Dino)(Paperback)

Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial

Mathematics for Machine Learning (Deisenroth Marc Peter)(Paperback)

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 courses, making it hard for data science or

A First Course in Machine Learning (Rogers Simon)(Pevná vazba)

'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 analysis in the simplest of settings, and goes

Ownership of Enterprise (Revised) (Hansmann Henry)(Paperback)

The investor-owned corporation is the conventional form for structuring large-scale enterprise in market economies. But it is not the only one. Even in the United States, noncapitalist firms play a vital role in many sectors. Employee-owned firms have long been prominent in the service

Machine Learning with Python for Everyone (Fenner Mark)(Paperback)

The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can

Advances in Financial Machine Learning (Lopez de Prado Marcos)(Pevná vazba)

Machine Learning in Financial Markets begins by structuring financial data in a way that is amenable to machine learning (ML) algorithms. Then, the author discusses how to conduct research with ML algorithms on that data and how to backtest your discoveries. Most of the problems and solutions are

Machine Learning with Tensorflow (Shukla Nishant)(Paperback)

Summary Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology

Introduction to Machine Learning with Applications in Information Security (Stamp Mark)(Pevná vazba)

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn't prove theorems, or otherwise dwell on mathematical theory

An Introduction to Machine Learning in Quantitative Finance (Ni Hao)(Paperback)

In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping

Machine Learning in Asset Pricing (Nagel Stefan)(Pevná vazba)

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments,

Practical Machine Learning in R (Nwanganga Fred)(Paperback)

Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning--a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit

Master Algorithm - How the Quest for the Ultimate Learning Machine Will Remake Our World (Domingos Pedro)(Paperback / softback)

Mathematics for Machine Learning (Deisenroth Marc Peter)(Pevná vazba)

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 courses, making it hard for data science or

Engineering Power!: Machines in the Sky (Barnham Kay)(Paperback / softback)

Discover the engineering behind how machines

Cloud Ethics: Algorithms and the Attributes of Ourselves and Others (Amoore Louise)(Paperback)

In Cloud Ethics Louise Amoore examines how machine learning algorithms are transforming the ethics and politics of contemporary society. Conceptualizing algorithms as ethicopolitical entities that are entangled with the data attributes of people, Amoore outlines how algorithms give incomplete

Hello World - How to be Human in the Age of the Machine (Fry Hannah)(Paperback / softback)

Hands-on Scala Programming: Learn Scala in a Practical, Project-Based Way (Li Haoyi)(Paperback)

Hands-on Scala teaches you how to use the Scala programming language in a practical, project-based fashion. This book is designed to quickly teach an existing programmer everything needed to go from 'hello world' to building production applications like interactive websites, parallel web crawlers,

Introducing the Ib Diploma Programme (Abrioux Marc)(Paperback)

An ideal reference guide to introducing the IB Diploma in your school. Schools wishing to introduce the IB Diploma Programme are faced with major investment in terms of time, effort and money in order to become authorised. They also need to articulate why that programme is the most suitable one for

Engineering Power!: Machines in Space (Barnham Kay)(Paperback / softback)

Discover the engineering behind how machines

Thoughtful Machine Learning with Python: A Test-Driven Approach (Kirk Matthew)(Paperback)

Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book

Learning in the Museum (Hein George E.)(Paperback)

Learning in the Museum examines major issues and shows how research in visitor studies and the philosophy of education can be applied to facilitate a meaningful educational experience in museums. Hein combines a brief history of education in public museums, with a rigorous examination of how the

Keras to Kubernetes: The Journey of a Machine Learning Model to Production (Rao Dattaraj)(Paperback)

Build a Keras model to scale and deploy on a Kubernetes cluster We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within