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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
1

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

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
1

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
1

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
1

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
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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
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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
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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

Machine Learning System Design - Arseny Kravchenko - Bog - Manning Publications - Plusbog.dk

Machine Learning System Design - Arseny Kravchenko - Bog - Manning Publications - Plusbog.dk

Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems. In Machine Learning System Design: With end-to-end examples you will learn: - - The big picture of machine learning system design - - Analyzing a problem space to identify the optimal ML solution - - Ace ML system design interviews - - Selecting appropriate metrics and evaluation criteria - - Prioritizing tasks at different stages of ML system design - - Solving dataset-related problems through data gathering, error analysis, and feature engineering - - Recognizing common pitfalls in ML system development - - Designing ML systems to be lean, maintainable, and extensible over time - Machine Learning System Design: With end-to-end examples is a practical guide for planning and designing successful ML applications. It lays out a clear, repeatable framework for building, maintaining, and improving systems at any scale. Authors Arseny Kravchenko and Valeri Babushkin have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You''ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system.

DKK 450.00
1

Real-World Machine Learning - Henrick Brink - Bog - Manning Publications - Plusbog.dk

Real-World Machine Learning - Henrick Brink - Bog - Manning Publications - Plusbog.dk

DESCRIPTION In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods. KEY FEATURES - - Accessible and practical introduction to machine learning - - Contains big-picture ideas and real-world examples - - Prepares reader to build and deploy powerful predictive systems - - Offers tips & tricks and highlights common pitfalls - AUDIENCE Code examples are in Python and R. No prior machine learning experience required. ABOUT THE TECHNOLOGY Machine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all demanding machine learning skills from their developers.

DKK 380.00
1

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
1

Machine Learning Algorithms in Depth - Vadim Smolyakov - Bog - Manning Publications - Plusbog.dk

Machine Learning Algorithms in Depth - Vadim Smolyakov - Bog - Manning Publications - Plusbog.dk

Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: - - Monte Carlo Stock Price Simulation - - Image Denoising using Mean-Field Variational Inference - - EM algorithm for Hidden Markov Models - - Imbalanced Learning, Active Learning and Ensemble Learning - - Bayesian Optimisation for Hyperparameter Tuning - - Dirichlet Process K-Means for Clustering Applications - - Stock Clusters based on Inverse Covariance Estimation - - Energy Minimisation using Simulated Annealing - - Image Search based on ResNet Convolutional Neural Network - - Anomaly Detection in Time-Series using Variational Autoencoders - Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.

DKK 555.00
1

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
1

MLOps Engineering at Scale - Carl Osipov - Bog - Manning Publications - Plusbog.dk

MLOps Engineering at Scale - Carl Osipov - Bog - Manning Publications - Plusbog.dk

Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML system’s infrastructure. Following a real-world use case for calculating taxi fares, you’ll learn how to get a serverless ML pipeline up and running using AWS services. Clear and detailed tutorials show you how to develop reliable, flexible, and scalable machine learning systems without time-consuming management tasks or the costly overheads of physical hardware. about the technology Your new machine learning model is ready to put into production, and suddenly all your time is taken up by setting up your server infrastructure. Serverless machine learning offers a productivity-boosting alternative. It eliminates the time-consuming operations tasks from your machine learning lifecycle, letting out-of-the-box cloud services take over launching, running, and managing your ML systems. With the serverless capabilities of major cloud vendors handling your infrastructure, you’re free to focus on tuning and improving your models. about the book Cloud Native Machine Learning is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You’ll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you’ll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, you’ll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform. When you’re done, you’ll have the tools to easily bridge the gap between ML models and a fully functioning production system. what''s inside - - Extracting, transforming, and loading datasets - - Querying datasets with SQL - - Understanding automatic differentiation in PyTorch - - Deploying trained models and pipelines as a service endpoint - - Monitoring and managing your pipeline’s life cycle - - Measuring performance improvements - about the reader For data professionals with intermediate Python skills and basic familiarity with machine learning. No cloud experience required. about the author Carl Osipov has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the world’s foremost experts in machine learning and also helped manage the company’s efforts to democratize artificial intelligence. You can learn more about Carl from his blog Clouds With Carl .

DKK 398.00
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Bayesian Optimization in Action - Quan Nguyen - Bog - Manning Publications - Plusbog.dk

Bayesian Optimization in Action - Quan Nguyen - Bog - Manning Publications - Plusbog.dk

Apply advanced techniques for optimising machine learning processes For machine learning practitioners confident in maths and statistics. Bayesian Optimization in Action shows you how to optimise hyperparameter tuning, A/B testing, and other aspects of the machine learning process, by applying cutting-edge Bayesian techniques. Using clear language, Bayesian Optimization helps pinpoint the best configuration for your machine-learning models with speed and accuracy. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn''t have to be difficult! Key features include: - - Train Gaussian processes on both sparse and large data sets - - Combine Gaussian processes with deep neural networks to make them flexible and expressive - - Find the most successful strategies for hyperparameter tuning - - Navigate a search space and identify high-performing regions - - Apply Bayesian Optimisation to practical use cases such as cost-constrained, multi-objective, and preference optimisation - - Use PyTorch, GPyTorch, and BoTorch to implement Bayesian optimisation - You will get in-depth insights into how Bayesian optimisation works and learn how to implement it with cutting-edge Python libraries. The book''s easy-to-reuse code samples will let you hit the ground running by plugging them straight into your own projects! About the technology Experimenting in science and engineering can be costly and time-consuming, especially without a reliable way to narrow down your choices. Bayesian Optimisation helps you identify optimal configurations to pursue in a search space. It uses a Gaussian process and machine learning techniques to model an objective function and quantify the uncertainty of predictions. Whether you''re tuning machine learning models, recommending products to customers, or engaging in research, Bayesian Optimisation can help you make better decisions faster.

DKK 459.00
1

Fighting Churn with Data - Carl Gold - Bog - Manning Publications - Plusbog.dk

Fighting Churn with Data - Carl Gold - Bog - Manning Publications - Plusbog.dk

Churn is the bane of any subscription business, such as content subscriptions, software as a service, and even ad-supported freemium apps. You can improve customer retention through product changes and targeted engagement campaigns based on data-driven interventions. This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers. Don’t let your hard-won customers vanish from subscription services, taking their money with them. In Fighting Churn with Data you’ll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. • Identifying processes suited to machine learning • Using machine learning to automate back office processes • Seven everyday business process projects • Using open source and cloud-based tools • Case studies for machine learning decision making For readers with basic data analysis skills, including Python and SQL.

DKK 468.00
1

Causal Inference for Data Science - Alex Ruiz De Villa - Bog - Manning Publications - Plusbog.dk

Causal Inference for Data Science - Alex Ruiz De Villa - Bog - Manning Publications - Plusbog.dk

When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning. In Causal Inference for Data Science you will learn how to: - - Model reality using causal graphs - - Estimate causal effects using statistical and machine learning techniques - - Determine when to use A/B tests, causal inference, and machine learning - - Explain and assess objectives, assumptions, risks, and limitations - - Determine if you have enough variables for your analysis - It''s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You''ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.

DKK 450.00
1

Data Without Labels - Vaibhav Verdhan - Bog - Manning Publications - Plusbog.dk

Data Without Labels - Vaibhav Verdhan - Bog - Manning Publications - Plusbog.dk

Discover all-practical implementations of the key algorithms and models for handling unlabelled data. Full of case studies demonstrating how to apply each technique to real-world problems. In Data Without Labels you’ll learn: - - Fundamental building blocks and concepts of machine learning and unsupervised learning - - Data cleaning for structured and unstructured data like text and images - - Clustering algorithms like kmeans, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering - - Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE - - Association rule algorithms like aPriori, ECLAT, SPADE - - Unsupervised time series clustering, Gaussian Mixture models, and statistical methods - - Building neural networks such as GANs and autoencoders - - Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling - - Association rule algorithms like aPriori, ECLAT, and SPADE - - Working with Python tools and libraries like sklearn, bumpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, andFflask - - How to interpret the results of unsupervised learning - - Choosing the right algorithm for your problem - - Deploying unsupervised learning to production - Data Without Labels introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You’ll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don’t get bogged down in theory—the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You’ll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Unsupervised learning and machine learning algorithms draw inferences from unannotated data sets. The self-organizing approach to machine learning is great for spotting patterns a human might miss. About the book Data Without Labels teaches you to apply a full spectrum of machine learning algorithms to raw data. You’ll master everything from kmeans and hierarchical clustering, to advanced neural networks like GANs and Restricted Boltzmann Machines. You’ll learn the business use case for different models, and master best practices for structured, text, and image data. Each new algorithm is introduced with a case study for retail, aviation, banking, and more—and you’ll develop a Python solution to fix each of these real-world problems. At the end of each chapter, you’ll find quizzes, practice datasets, and links to research papers to help you lock in what you’ve learned and expand your knowledge. About the reader For developers and data scientists. Basic Python experience required. About the author Vaibhav Verdhan is a seasoned data science professional with rich experience across geographies and domains. He has led multiple engagements in machine learning and artificial intelligence. A leading industry expert, Vaibhav is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland where he works as a principal data scientist.

DKK 515.00
1

Natural Language Processing in Action - Hapke Hannes - Bog - Manning Publications - Plusbog.dk

Natural Language Processing in Action - Hapke Hannes - Bog - Manning Publications - Plusbog.dk

Description Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. In Natural Language Processing in Action , readers explore carefully chosen examples and expand their machine''s knowledge which they can then apply to a range of challenges. Key Features • Easy-to-follow • Clear examples • Hands-on-guide Audience A basic understanding of machine learning and some experience with a modern programming language such as Python, Java, C++, or JavaScript will be helpful. About the technology Natural Language Processing (NLP) is the discipline of teaching computers to read more like people, and readers can see examples of it in everything from chatbots to the speech-recognition software on their phone. Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans. Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning. Cole Howard is a carpenter and writer turned Deep Learning expert.

DKK 382.00
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Deep Reinforcement Learning in Action - Brandon Brown - Bog - Manning Publications - Plusbog.dk

Deep Reinforcement Learning in Action - Brandon Brown - Bog - Manning Publications - Plusbog.dk

Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Key features • Structuring problems as Markov Decision Processes • Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them • Applying reinforcement learning algorithms to real-world problems Audience You’ll need intermediate Python skills and a basic understanding of deep learning. About the technology Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but that’s not all it can do! Alexander Zai is a Machine Learning Engineer at Amazon AI working on MXNet that powers a suite of AWS machine learning products. Brandon Brown is a Machine Learning and Data Analysis blogger at outlace.com committed to providing clear teaching on difficult topics for newcomers.

DKK 415.00
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Grokking Deep Reinforcement Learning - Miguel Morales - Bog - Manning Publications - Plusbog.dk

Grokking Deep Reinforcement Learning - Miguel Morales - Bog - Manning Publications - Plusbog.dk

Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You''ll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. • Foundational reinforcement learning concepts and methods • The most popular deep reinforcement learning agents solving high-dimensional environments • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.

DKK 398.00
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Optimization Algorithms - Alaa Khamis - Bog - Manning Publications - Plusbog.dk

Optimization Algorithms - Alaa Khamis - Bog - Manning Publications - Plusbog.dk

Solve design, planning, and control problems using modern machine learning and AI techniques. In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn: - - Machine learning methods for search and optimization problems - - The core concepts of search and optimization - - Deterministic and stochastic optimization techniques - - Graph search algorithms - - Nature-inspired search and optimization algorithms - - Efficient trade-offs between search space exploration and exploitation - - State-of-the-art Python libraries for search and optimization - About the technology: Search and optimization algorithms are powerful tools that can help practitioners find optimal or near-optimal solutions to a wide range of design, planning and control problems. When you open a route planning app, call for a rideshare, or schedule a hospital appointment, an AI algorithm works behind the scenes to make sure you get an optimized result. This guide reveals the classical and modern algorithms behind these services.

DKK 630.00
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Real-World Natural Language Processing - Masatoshi Hagiwara - Bog - Manning Publications - Plusbog.dk

Real-World Natural Language Processing - Masatoshi Hagiwara - Bog - Manning Publications - Plusbog.dk

Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps. about the technology Natural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines. about the book Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq. In this practical guide, you’ll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you’ll use in all different kinds of NLP programs. By the time you’re done, you’ll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems. what''s inside - - Design, develop, and deploy basic NLP applications - - NLP libraries such as AllenNLP and Fairseq - - Advanced NLP concepts such as attention and transfer learning - about the reader Aimed at intermediate Python programmers. No mathematical or machine learning knowledge required. about the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009, focusing on Natural Language Processing and machine learning. He has interned at Google and Microsoft Research, and worked at Baidu Japan, Duolingo, and Rakuten Institute of Technology. He now runs his own consultancy business advising clients, including startups and research institutions.

DKK 448.00
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Deep Learning with R, Second Edition - Francois Chollet - Bog - Manning Publications - Plusbog.dk

Deep Learning with R, Second Edition - Francois Chollet - Bog - Manning Publications - Plusbog.dk

Deep learning from the ground up using R and the powerful Keras library! In Deep Learning with R, Second Edition you will learn: - - Deep learning from first principles - - Image classification and image segmentation - - Time series forecasting - - Text classification and machine translation - - Text generation, neural style transfer, and image generation - Deep Learning with R, Second Edition shows you how to put deep learning into action. It''s based on the revised new edition of François Chollet''s bestselling Deep Learning with Python . All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. about the technology Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. what''s inside - - Image classification and image segmentation - - Time series forecasting - - Text classification and machine translation - - Text generation, neural style transfer, and image generation - about the reader For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.

DKK 524.00
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