123 resultater (0,41172 sekunder)

Vis alle filtre

Mærke

Butik

Pris (EUR)

Nulstil filter

Produkter
Fra
Butikker

Distributed Machine Learning Patterns - Yuan Tang - Bog - Manning Publications - Plusbog.dk

Distributed Machine Learning Patterns - Yuan Tang - Bog - Manning Publications - Plusbog.dk

Practical patterns for scaling machine learning from your laptop to a distributed cluster. In Distributed Machine Learning Patterns you will learn how to: - - Apply distributed systems patterns to build scalable and reliable machine learning projects - - Construct machine learning pipelines with data ingestion, distributed training, model serving, and more - - Automate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows - - Make trade offs between different patterns and approaches - - Manage and monitor machine learning workloads at scale - Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In Distributed Machine Learning Patterns , you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In it, you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. about the technology Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure. about the book Distributed Machine Learning Patterns is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud. Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Real-world scenarios provide clear examples of how to apply each pattern, alongside the potential trade offs for each approach. Once you''ve mastered these cutting edge techniques, you''ll put them all into practice and finish up by building a comprehensive distributed machine learning system.

DKK 459.00
3

Grokking Machine Learning - Luis Serrano - Bog - Manning Publications - Plusbog.dk

Grokking Machine Learning - Luis Serrano - Bog - Manning Publications - Plusbog.dk

It''s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily available machine learning tools! In Grokking Machine Learning , expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you’re grokking as you go. You’ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Key Features · Different types of machine learning, including supervised and unsupervised learning · Algorithms for simplifying, classifying, and splitting data · Machine learning packages and tools · Hands-on exercises with fully-explained Python code samples For readers with intermediate programming knowledge in Python or a similar language. About the technology Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art. Luis G. Serrano has worked as the Head of Content for Artificial Intelligence at Udacity and as a Machine Learning Engineer at Google, where he worked on the YouTube recommendations system. He holds a PhD in mathematics from the University of Michigan, a Bachelor and Masters from the University of Waterloo, and worked as a postdoctoral researcher at the University of Quebec at Montreal. He shares his machine learning expertise on a YouTube channel with over 2 million views and 35 thousand subscribers, and is a frequent speaker at artificial intelligence and data science conferences.

DKK 448.00
3

Machine Learning with TensorFlow - Nishant Shukla - Bog - Manning Publications - Plusbog.dk

Machine Learning with TensorFlow - Nishant Shukla - Bog - Manning Publications - Plusbog.dk

DESCRIPTION Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you''re just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world. KEY FEATURES • Lots of diagrams, code examples, and exercises • Solves real-world problems with TensorFlow • Uses well-studied neural network architectures • Presents code that can be used for the readers’ own applications AUDIENCE This book is for programmers who have some experience with Python and linear algebra concepts like vectors and matrices. No experience with machine learning is necessary. ABOUT THE TECHNOLOGY Google open-sourced their machine learning framework called TensorFlow in late 2015 under the Apache 2.0 license. Before that, it was used proprietarily by Google in its speech recognition, Search, Photos, and Gmail, among other applications. TensorFlow is one the most popular machine learning libraries.

DKK 349.00
3

Privacy-Preserving Machine Learning - G. Samaraweera - Bog - Manning Publications - Plusbog.dk

Privacy-Preserving Machine Learning - G. Samaraweera - Bog - Manning Publications - Plusbog.dk

Keep sensitive user data safe and secure, without sacrificing theaccuracy of your machine learning models. In Privacy Preserving Machine Learning , you will learn: - - Differential privacy techniques and their application insupervised learning - - Privacy for frequency or mean estimation, Naive Bayes classifier,and deep learning - - Designing and applying compressive privacy for machine learning - - Privacy-preserving synthetic data generation approaches - - Privacy-enhancing technologies for data mining and database applications - Privacy Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You''ll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels and seniorities will benefit from incorporating these privacy-preserving practices into their model development.

DKK 500.00
3

Ensemble Methods for Machine Learning - Gautam Kunapuli - Bog - Manning Publications - Plusbog.dk

Ensemble Methods for Machine Learning - Gautam Kunapuli - Bog - Manning Publications - Plusbog.dk

Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you''ll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.

DKK 459.00
3

Managing Machine Learning Projects - Simon Thompson - Bog - Manning Publications - Plusbog.dk

Managing Machine Learning Projects - Simon Thompson - Bog - Manning Publications - Plusbog.dk

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 requirements - - Setting up the infrastructure for the project and resourcing a team - - Working with clients and other stakeholders - - Dealing with data resources and bringing them into the project for use - - Handling the lifecycle of models in the project - - Managing the application of ML algorithms - - Evaluating the performance of algorithms and models - - Making decisions about which models to adopt for delivery - - Taking models through development and testing - - Integrating models with production systems to create effective applications - - Steps and behaviours for managing the ethical implications of ML technology - About the technology Companies of all shapes, sizes, and industries are investing in machine learning (ML). Unfortunately, around 85% of all ML projects fail. Managing machine learning projects requires adopting a different approach than you would take with standard software projects. You need to account for large and diverse data resources, evaluate and track multiple separate models, and handle the unforeseeable risk of poor performance. Never fear — this book lays out the unique practices you will need to ensure your projects succeed!

DKK 459.00
3

Machine Learning Systems - Jeff Smith - Bog - Manning Publications - Plusbog.dk

Machine Learning Systems - Jeff Smith - Bog - Manning Publications - Plusbog.dk

Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you’re building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside - - Working with Spark, MLlib, and Akka - - Reactive design patterns - - Monitoring and maintaining a large-scale system - - Futures, actors, and supervision - About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING 1) 1) Learning reactive machine learning 1) 1) Using reactive tools 1) PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM 1) 1) Collecting data 1) 1) Generating features 1) 1) Learning models 1) 1) Evaluating models 1) 1) Publishing models 1) 1) Responding 1) PART 3 - OPERATING A MACHINE LEARNING SYSTEM 1) 1) Delivering 1) 1) Evolving intelligence 1)

DKK 370.00
1

Human-in-the-Loop Machine Learning - Robert Munro

Human-in-the-Loop Machine Learning - Robert Munro

Robert Munro has built Annotation, Active Learning, and machine learning systems with machine learning-focused startups and with larger companies including Amazon, Google, IBM, and most major phone manufacturers. If you speak to your phone, if your car parks itself, if your music is tailored to your taste, or if your news articles are recommended for you, then there is a good chance that Robert contributed to this experience. Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning..

DKK 576.95
2

Machine Learning Engineering in Action - Ben Wilson - Bog - Manning Publications - Plusbog.dk

Machine Learning Engineering in Action - Ben Wilson - Bog - Manning Publications - Plusbog.dk

Field-tested tips, tricks, and design patterns for building MachineLearning projects that are deployable, maintainable, and secure from concept toproduction. In Machine Learning Engineering inAction , you will learn: - - Evaluatingdata science problems to find the most effective solution - - Scopinga machine learning project for usage expectations and budget - - Processtechniques that minimize wasted effort and speed up production - - Assessinga project using standardized prototyping work and statistical validation - - Choosingthe right technologies and tools for your project - - Makingyour codebase more understandable, maintainable, and testable - - Automatingyour troubleshooting and logging practices - Databricks solutions architect BenWilson lays out an approach to building deployable, maintainable productionmachine learning systems. You''ll adopt software development standards thatdeliver better code management, and make it easier to test, scale, and evenreuse your machine learning code!

DKK 445.00
3

Automated Machine Learning in Action - Xia Hu - Bog - Manning Publications - Plusbog.dk

Automated Machine Learning in Action - Xia Hu - Bog - Manning Publications - Plusbog.dk

Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and KerasTuner. In Automated Machine Learning in Action you will learn how to: - - Improve a machine learning model by automatically tuning its hyperparameters - - Pick the optimal components for creating and improving your pipelines - - Use AutoML toolkits such as AutoKeras and Keras Tuner - - Design and implement search algorithms to find the best component for your ML task - - Accelerate the AutoML process with data-parallel, model pre-training, and other techniques - Automated Machine Learning in Action reveals how premade machine learning components can automate time-consuming ML tasks.It''s written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. You''ll use tools like AutoKeras to create pipelines that automatically select the best approach for your task, remove the burden of manual tuning, and can even be implemented by machine learning novices!

DKK 388.00
1

Machine Learning for Tabular Data - Mark Ryan - Bog - Manning Publications - Plusbog.dk

Machine Learning for Tabular Data - Mark Ryan - Bog - Manning Publications - Plusbog.dk

Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets, databases, and logs. You’ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline. Machine Learning for Tabular Data will teach you how to: • Pick the right machine learning approach for your data • Apply deep learning to tabular data • Deploy tabular machine learning locally and in the cloud • Pipelines to automatically train and maintain a model Machine Learning for Tabular Data covers classic machine learning techniques like gradient boosting, and more contemporary deep learning approaches. By the time you’re finished, you’ll be equipped with the skills to apply machine learning to the kinds of data you work with every day. Foreword by Antonio Gulli . Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Machine learning can accelerate everyday business chores like account reconciliation, demand forecasting, and customer service automation—not to mention more exotic challenges like fraud detection, predictive maintenance, and personalized marketing. This book shows you how to unlock the vital information stored in spreadsheets, ledgers, databases and other tabular data sources using gradient boosting, deep learning, and generative AI. About the book Machine Learning for Tabular Data delivers practical ML techniques to upgrade every stage of the business data analysis pipeline. In it, you’ll explore examples like using XGBoost and Keras to predict short-term rental prices, deploying a local ML model with Python and Flask, and streamlining workflows using large language models (LLMs). Along the way, you’ll learn to make your models both more powerful and more explainable. What''s inside • Master XGBoost • Apply deep learning to tabular data • Deploy models locally and in the cloud • Build pipelines to train and maintain models About the reader For readers experienced with Python and the basics of machine learning. About the author Mark Ryan is the AI Lead of the Developer Knowledge Platform at Google. A three-time Kaggle Grandmaster, Luca Massaron is a Google Developer Expert (GDE) in machine learning and AI. He has published 17 other books. Table of Contents Part 1 1 Understanding tabular data 2 Exploring tabular datasets 3 Machine learning vs. deep learning Part 2 4 Classical algorithms for tabular data 5 Decision trees and gradient boosting 6 Advanced feature processing methods 7 An end-to-end example using XGBoost Part 3 8 Getting started with deep learning with tabular data 9 Deep learning best practices 10 Model deployment 11 Building a machine learning pipeline 12 Blending gradient boosting and deep learning A Hyperparameters for classical machine learning models B K-nearest neighbors and support vector machines

DKK 674.00
1

Graph-Powered Machine Learning - Alessandro Negro - Bog - Manning Publications - Plusbog.dk

Graph-Powered Machine Learning - Alessandro Negro - Bog - Manning Publications - Plusbog.dk

At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you’ll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features · The lifecycle of a machine learning project · Three end-to-end applications · Graphs in big data platforms · Data source modeling · Natural language processing, recommendations, and relevant search · Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it’s important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.

DKK 476.00
1